Recent natural language generation systems have made remarkable progress in producing well-formed coherent text, especially with the massive pretrained language models (LMs) Radford et al. (2019); Brown et al. (2020); Lewis et al. (2020); Raffel et al. (2019). Those models are typically trained with maximum likelihood estimation (MLE) on large supervised data. Despite its efficiency and successful outcomes, the standard training method suffers from limited applicability to many emerging text generation problems, where little or no standard supervised data is available. Prominent examples include learning to generate prompts to control the massive LMs Yin et al. (2019); Shin et al. (2020); Zhong et al. (2021), learning text generation from noisy or even negative data, learning to generate adversarial text attacks for robustness study Wallace et al. (2019); Atanasova et al. (2020), and others, where people have to devise specialized algorithms due to the failure of the standard MLE.
On the other hand, reinforcement learning (RL) Sutton and Barto (2018) offers an alternative principled formulation for learning in general. The framework enjoys added flexibility by allowing users to plug in arbitrary reward functions. Instead of (blindly) imitating the training data, the model is trained to maximize the rewards to possess desired generation abilities. However, RL by far has made limited success for training text generation (Choshen et al., 2020; Wu et al., 2018). A popular family of RL algorithms studied extensively for text generation is the policy-based Williams (1992) or actor-critic based Bahdanau et al. (2016); Rennie et al. (2017) algorithms, with policy gradient (PG) (Ranzato et al., 2015; Li et al., 2016; Rennie et al., 2017; Tan et al., 2018; Pasunuru and Bansal, 2018; Paulus et al., 2018) being the most prevalent example. Those algorithms train the model with on-policy
updates, i.e., the text samples used for estimating policy gradients are from the target model itself. Due to the exponentially large space of sequences, on-policy updates often suffer from extremely high variance and low data efficiency (e.g., most model samples are not useful for learning). Thus directly training with PG from scratch is usually impossible. In practice, the model has to be initialized by MLE training, followed by PG as finetuning, which often leads to limited improvement(Choshen et al., 2020; Wu et al., 2018).
To overcome the shortcomings, another set of work has resorted to off-policy RL. The key advantage of off-policy updates is that samples from other sources, such as human-written text, can be used, making them more data efficient than on-policy methods. Previous work has used either importance weighted PG Pang and He (2021); Zhou et al. (2017); Kandasamy et al. (2017) or -learning based algorithms Guo (2015); Jaques et al. (2020); Narasimhan et al. (2015). However, the off-policy methods have been considered to be less stable. For example, the -learning performance relies heavily on how accurate the learned -function assesses the quality of intermediate subsequences – a challenging task due to the sparse reward signals (e.g., reward is received only after the whole sequence is generated). Further, previous work has largely focused on the extreme of using only off-policy data, mostly for offline training of chatbots Jaques et al. (2020). As a result, the opportunity of directly improving the reward (as in on-policy updates) for other rich tasks is missed.
In this paper, we develop a new RL formulation for text generation that addresses the above issues. Specifically, we reframe the text generation problem from the soft -learning perspective Haarnoja et al. (2017); Schulman et al. (2017), which enables us to further take advantage of the latest successful techniques from the RL literature. In particular, we introduce the principled path consistency learning Nachum et al. (2017), that (1) offers a natural way to train the model with both on-policy and off-policy updates, hence combining the best of the two strategies, and (2) bridges the sparse reward signals directly to supervise the function learning, leading to more accurate estimation and credit assignment.
The generality of the proposed learning framework allows us to train text generation in a wide range of applications: (1) With noisy and negative training examples for entailment generation, our approach manages to greatly improve upon the data and generate accurate entailment text; (2) The method also applies to train an effective generator for black-box adversarial attacks
against a popular entailment classifier; (3) We train aprompt generator with our algorithm to achieve controllable generation of pretrained LMs in terms of topics. On all the three tasks, our approach consistently improves over both task-specialized algorithms and other general RL methods such as PG. Finally, (4) we study on the standard supervised tasks (E2E (Novikova et al., 2017), CommonGen (Lin et al., 2020)) where MLE prevails. We show that our approach is competitive to train text generation models from scratch, which was usually impossible for previous RL algorithms.
The goal of text generation is to produce coherent text of certain properties for a given task, where is a token from a vocabulary , and is the text length. The generation can condition on arbitrary input context, which we omit for simplicity of notations. We aim to learn a generation model which is typically decomposed autoregressively as , where
is the prefix, and the distribution at each step is obtained by applying the softmax function on the output logits:
Here is the logit of token computed by the generation model.
2.1 Maximum Likelihood Estimation (MLE)
Given a training example , MLE trains the model by maximizing the data log-likelihood. More concretely. At every time-step , the ground-truth prefix given as input to the model. The output at the current time-step is then compared against the corresponding ground-truth token via cross-entropy. The objective leads to the following update:
Despite its popularity, MLE-based training only applies when supervised data is available, and cannot be used to optimize arbitrary task metrics (e.g., BLEU, entailment score) which are typically the goal in many text generation tasks.
2.2 Reinforcement Learning (RL) Formulation for Text Generation
To formulate text generation as an RL problem, we consider the following finite-time Markov Decision Process (MDP). At each time step, let the “state” be , namely the partial sequence generated so far. The model, also known as the “agent”, takes as input the current state and outputs a token, also called “action”, according to a policy . The agent then receives a reward and (deterministically) transitions to next state . Let the trajectory be defined as , the agent’s objective is to maximize the accumulative reward,
where is the discount factor. In text generation, the reward signal is usually sparse, i.e., and the agent receives a non-zero reward only after it generates the full sequence. A central concept in RL is the state-action value function (-function) of policy , defined as , which is the expected future reward of taking action (i.e., generating token ) in state and continuing with the policy . There are two major families of RL approaches to parameterizing and training the agent as below.
The first family is the policy-based techniques that directly parameterize the policy with parameters . Thus the policy exactly corresponds to the above generation model . To learn the parameters , policy gradient (PG) is one of the most widely used algorithms for text generation (Ranzato et al., 2015). It optimizes the cumulative reward with the policy gradient:
where is the estimated value with sample . Notice that the expectation is taken w.r.t. the policy , which makes PG an on-policy algorithm, meaning that the sample needs to come from the the current policy itself. Intuitively, the update is analogous to maximizing the likelihood of the sampled sequences weighted by .
In practice, however, optimizing this objective alone from scratch is unlikely going to work because most samples are just gibberish with zero reward, failing to provide meaningful training signals for updating the policy. Previous literature either initializes the policy with MLE training, and/or use a combination of MLE and PG updates, which often leads to marginal gains in practice Wu et al. (2018); Choshen et al. (2020).
The second family is the value-based techniques, such as -learning, that implicitly learn the policy by approximating the value directly. Specifically, let denote the optimal value over policies. Thus the optimal policy is simply taking the action of maximal value at each state, i.e., , where is the indicator function that takes if and otherwise. The approximation of is based on the well-known Bellman temporal consistency:
Recall that in the context of text generation, , i.e., the concatenation of the tokens in and the token . Deep -learning (Mnih et al., 2013) parameterizes the -function as
(e.g., a neural network), and train the parameters by minimizing the following regression objective:
where is the parameters of the target -network, which is a slow copy of and considered as constant for gradient computation of . Here is an behavior policy which can be an arbitrary distribution over text, such as the data distribution or replay buffer (Mnih et al., 2013). This makes -learning an off-policy algorithm because of its ability to use samples coming from another policy. After learning , we can induce a policy from it as above that takes at each state . Jaques et al. (2017) instead sample tokens from the softmax function applied to .
However, the training can be unstable and inefficient due to several challenges: (1) The bootstrapping nature of the above regression problem can make the training unstable. That is, the regression target itself is derived from the -function to be learned (Kumar et al., 2019). The problem is exacerbated in the presence of sparse reward in text generation, where the real observed signal is zero for all intermediate ; (2) The large action space (e.g., ) in text generation results in slow updates. In particular, notice that Eq.(6) applies the gradient update to the -value of the only one particular token (out of the candidate tokens in the vocabulary), making the training inefficient; (3) Besides, pure off-policy updates could be highly sensitive to the quality of training data, and miss the opportunity of on-policy exploration that maximizes the reward of interest in a more direct way.
3 The Soft -Learning Framework
In this section, we combat the difficulties of previous RL methods by introducing the soft -learning (SQL) formulation of text generation. We show that the formulation is seamlessly compatible with the common architecture of text generation model (Eq.1), permitting easy implementation (§3.1). The formulation further allows us to integrate the latest advances in RL, in particular path consistency learning (Nachum et al., 2017) that makes the RL training efficient and stable in practice (§3.2).
3.1 Soft -Learning Formulation for Text Generation
Soft -learning (Haarnoja et al., 2017; Schulman et al., 2017; Nachum et al., 2017) is an maximum-entropy (MaxEnt) extension to the standard (hard) -learning Mnih et al. (2015); Sutton and Barto (2018). Under this framework, the agent is encouraged to optimize the reward while staying as stochastic as possible, with the following objective:
which augments the vanilla in Eq.(3) with the additional Shannon entropy term with coefficient . We can assume without loss of generality, as it can be folded into the reward function by scaling the latter with . This framework is appealing because it seamlessly connects the -values to the familiar output logits of a text generation model, which enables straightforward implementation of the SQL formulation.
-values as Generation Model Logits
We show the connection of the -values with the logits, i.e., the model outputs right before the layer. Concretely, with the SQL objective in Eq.(7), the following relationship between optimal policy and action-value holds (Haarnoja et al., 2017; Schulman et al., 2017):
This form is highly reminiscent of the layer of the generation model in Eq.(1). The connection suggests that we can naturally parameterize the -function in SQL as the generation model logit function, i.e., . In other words, the model output , originally interpretted as the “logit” of token given the preceding tokens , is now re-interpretted as the -value of action in state . When achieving optimality, , namely , represents the best possible future reward achievable by generating token in state . Similarly, the full generation model in Eq.(1) that applies to now precisely corresponds to the policy induced from . That is,
We could further gain even more intuitive/concise interpretation of the above generation policy from the lens of advantage function (Sutton and Barto, 2018). Specifically, in SQL, the optimal state-value function is the log-normalizer of the optimal -values (Haarnoja et al., 2017; Schulman et al., 2017), i.e.,
This allows us to rewrite Eq.(8) into a more concise form:
where is the optimal advantage function. The equation says that the optimal policy generates a token in state according to the token’s advantage.
3.2 Learning -function via Path Consistency Learning (PCL)
The above section has discussed parameterizing the -function with the common generation model with parameters . Now we describe how to learn the function within the SQL framework. Intuitively, learning is related to the credit assignment problem in text generation: given a sparse sequence-level reward , how do we properly assign credits to tokens (actions) taken along the way?
In the following, we first discuss the vanilla training method based on the “temporal consistency” as in the standard -learning (Eq.5). We then introduce a more efficient method based on another optimality property of “path consistency” (Nachum et al., 2017) that enables fast and effective updates for the -function given sparse reward.
3.2.1 Vanilla Training with Temporal Consistency
Much like the Bellman temporal consistency in standard -learning (Eq.5), in SQL, the optimal action-value function follows the softmax form of the temporal consistency Ziebart et al. (2008); Ziebart (2010); Fox et al. (2016); Nachum et al. (2017):
We thus can again derive a bootstrapping-like regression objective similar to the standard -learning (Eq.6):
Recall that is an arbitrary behavior policy (e.g., data distribution), and is the target -network which is a slow copy of the to be learned and is held fixed during the gradient updates. However, the above objective is inefficient due to exact the same reasons as in standard -learning discussed earlier, namely the unstable per-step bootstrapping-style training with sparse reward signals, plus the slow updates w.r.t only one token out of the large vocabulary (action space).
3.2.2 Efficient Training with Path Consistency
We instead derive the new gradient update rule following the unified path consistency learning (PCL) Nachum et al. (2017) which addresses the above two challenges. In particular, the PCL-based training updates -values of all tokens at once through a connection between the value function and the induced policy. More specifically, it is shown in Nachum et al. (2017) that the optimal policy (Eq.8) and the optimal state value function (Eq.10) in SQL must satisfy the following consistency property for all states and actions:
Accordingly, the PCL-based training attempts to encourage the satisfaction of the consistency with the following regression objective:
where is the induced policy defined in Eq.(9); and is defined similarly as in Eq.(10) but depends on the target network. Please see Figure 2 (left) for an illustration. Crucially, notice that the gradient update is applied to through the term which explicitly involves the -values of all tokens in the vocabulary. This shows an important difference from the above vanilla training where is updated only through the particular token. The PCL training thus offers more efficient updates for the function.
Comparison with MLE Objective
Before moving on to a further extension of the training, it is interesting to take a closer look at the above objective and compare with the common MLE training (§2.1). Specifically, we notice the relations between the optimal , , and functions: , where the first equation is the definition of (see Eq.10) and the second equation is due to Eqs.(12) and (10). We thus can see the regression target in the above objective as an approximation to the advantage function: . Therefore, by optimizing the regression objective,
, which is the log probability of generating tokengiven preceding tokens , is encouraged to match the approximate advantage value , no more and no less. This is different from the objective of MLE where the model is trained to (blindly) increase the probability of the observed token given and decrease the probability of the rest.
Multi-step PCL for Sparse Reward
The above PCL objective Eq.(15) does not resolve the potential instability issue due to the bootstrapped value and the sparse reward (i.e., for ). To this end, we additionally incorporate the multi-step variant of the PCL training (Nachum et al., 2017). Specifically, by applying a telescoping sum on the consistency equation (Eq.14) starting from up to , we arrive at the multi-step temporal consistency:
where the value of past-terminal state is zero, ; and the rewards are only available at the end, . We can then come to the following multi-step objective function,
We can see the objective side-steps the need to bootstrap intermediate value functions for . Instead, it directly uses the non-zero end reward to derive the update for . Please see Figure 2 (right) for an illustration.
On- and Off-policy Training
Finally, we highlight that the behavior policy involved in the objectives Eqs.(15) and (17) can be an arbitrary policy (i.e., distribution over text sequences), from which we can draw trajectories (i.e., text samples). For example, can be a (possibly noisy) text dataset, or a set of text samples produced by other generation models, resulting in off-policy training. We can also set to be the current generation model to be learned, resulting in on-policy training. In practice, we usually first train the model with only off-policy data for warming up, and then continue with joint on- and off-policy training to further maximize the reward.
Algorithm 1 summarizes the resulting SQL framework for efficient training of text generation (where we show joint on- and off-policy updates).
4 Applications and Experiments
We show broad applications of the general RL framework to a variety of problems where no clean supervision data is available, including learning with noisy or even negative data (§4.1), generating adversarial text attacks (§4.2), and generating prompts to steer pretrained LMs (§4.3). Our approach shows substantial improvement over both previous RL algorithms and specialized methods specific to each individual tasks. We also study the performance on standard supervised generation tasks (§4.4) and show our RL algorithm can train models from scratch in a stable way, achieving competitive results with MLE training. For each of the experiments, we provide detailed configurations in the appendix.
4.1 Learning from Noisy (Negative) Text
The popular MLE algorithm learns by (blindly) imitating training data, and thus often requires high-quality training examples. However, for text generation tasks with an enormous output space, it is often expensive to curate clean quality data. It is thus highly desirable to be able to learn from data with noises, or even negative examples. With the guidance of task metrics (rewards), the model can even learn to “outperform” the training data and achieve desired generation behaviors.
To this end, we consider the task of entailment generation Pasunuru and Bansal (2017). Given a sentence (premise), the goal of the task is to generate a new sentence (hypothesis) that logically follows the premise. For example, given source sentence ‘‘Sophie is walking a dog outside her house’’, the hypotheses ‘‘Sophie is outdoor’’ and ‘‘Sophie is walking a dog’’ are considered entailed, but ‘‘Sophie is inside her house’’ is not and even is a negative (contradictive) sentence.
We study using the SNLI datasetBowman et al. (2015), a dataset commonly used in training an entailment classifier. The original dataset contains (premise, hypothesis) sentence pairs, where the hypothesis may or may not entail the premise. We sub-sampled training examples from the corpus such that the hypotheses have an average entailment probability of only in terms of the premises, and over examples have entailment probabilities less than , which can be seen as negative (contradictive) examples. The resulting training set poses a significant challenge for the models to learn from the noises. We present more details of the dataset in the appendix.
Baselines and Setup.
The entailment generation model takes as input a premise and generates a hypothesis. We compare our training approach with several baselines, including (1) the standard MLE training (MLE), (2) PG with MLE initialization (MLE+PG), and (3) one of the latest text-generation RL algorithms GOLD- Pang and He (2021) which is a pure off-policy method based on importance-sampling PG. To ablate the effect of multi-step training (§3.2), we additionally compare with a simplified variant of our approach that uses only vanilla single-step PCL training (SQL (single)).
The RL algorithms (including PG and ours) permit us to plug in arbitrary reward functions to drive learning. Based on the goal of the task, we use the following intuitive rewards to ensure entailment accuracy and language quality: (1) a robust entailment classifier (Nie et al., 2020) that measures the entailment score of a generation in terms of the input premise, (2) a GPT-2 language model Radford et al. (2019) that measures the log-likelihood of the generation as an indicator of language quality, and (3) BLEU score w.r.t the input premises as another language quality reward that avoids trivial outputs. We sum together all rewards with weights . For all experiments in this and the following sections, we use a transformer model Vaswani et al. (2017)
based on Texar-PytorchHu et al. (2019) by default, with hidden dimension, blocks, and heads.
We evaluate generation results in terms of entailment rate, language quality (perplexity), and diversity which is measured by the Shannon entropy over unigrams and bigrams (, ) (Gehrmann et al., 2021). Since text generation models intrinsically trade off diversity and quality (Caccia et al., 2019; Hashimoto et al., 2019), we vary the generation diversity by generating samples via top- sampling (Holtzman et al., 2019) with different values, and plot the entailment rate and perplexity against diversity, respectively. We also evaluate the samples produced by beam-search decoding.
Figure 3 shows the results. First, notice that MLE performs poorly. This is not surprising as the training data contain noisy/negative examples. Similarly, since the pure off-policy algorithm GOLD- relies heavily on the data distribution, we observed that it achieves sub-optimal performance. The on-policy PG with MLE initialization gives better entailment rate. In comparison, our full SQL framework achieves the best entailment-diversity trade-off. The comparison between SQL and SQL (single) highlights the importance of having the multi-step objective which directly uses the end reward rather than bootstrapping intermediate -values for supervision.
4.2 Black-box Universal Adversarial Attacks
We next study the application of our approach to a very different problem, namely generating text adversarial attacks, where again no supervised data is available. Adversarial attacks is an increasingly important research topic as they reveal models’ vulnerabilities and flaws. This is especially true for universal attacks Wallace et al. (2019); Atanasova et al. (2020), where we want to generate universal examples that trick the model on all possible inputs. For instance, consider the context of entailment classification where the classifier takes as inputs a premise sentence and a hypothesis sentence, and predicts the probability of the hypothesis entails the premise. Our goal is to find universal human-readable hypotheses that are going to be classified as “entailment” with as high probability as possible, regardless of the input premises. This is a more challenging setting compared to previous instance-specific attack Morris et al. (2020); Jin et al. (2020); Ebrahimi et al. (2017) where the attack model conditions on a premise and generates an adversarial hypothesis specific to the premise.
Dataset, Baselines, and Setup.
We study the task of attacking an entailment classifier. In particular, we aim to attack one of the most popular entailment classifiers on HuggingFaceHub.222https://github.com/pytorch/fairseq/tree/master/examples/roberta, which is ranked #1 as of May 20, 2021 based on https://huggingface.co/models?search=nli. The attack generation model generates adversarial text without conditioning on any inputs so that the generated attacks are universal to all premises. The generation model is trained with mostly the same setting as in §4.1
, where the entailment classifier to be attacked is used as entailment score reward functions. Besides, we additionally include a token-level repetition penalty reward, which empirically benefits readability. Finally, we use the MultiNLI datasetWilliams et al. (2018) which includes more diverse examples than the SNLI used above.
We compare our SQL with MLE+PG. We use all hypotheses in the MultiNLI dataset as the training data for the MLE training in MLE+PG and the off-policy updates for our SQL. We do not compare with previous specialized adversarial text attack methods, because they either are not applicable to the universal attack setting Morris et al. (2020); Jin et al. (2020); Ebrahimi et al. (2017), or were not designed to generate human-readable sentences (Wallace et al., 2019). Besides, it is worth noting that the general RL algorithms have an additional advantage of doing black-box attacks. That is, the algorithms only require the ability to query the entailment classifier for entailment probability, without need of knowing the internal structure of the classifier (e.g., for computing gradients) as in previous attack algorithms (Ebrahimi et al., 2017; Wallace et al., 2019).
|MLE+PG||it ’s .||90.48|
|SQL (ours)||the person saint-pierre-et-saint-paul is saint-pierre-et-saint-paul .||97.40|
To explore the diversity-quality trade-off as in §4.1, we similarly generate samples from models using various values in top- decoding, and plot the entailment rate and perplexity against diversity, respectively. Figure 4 shows the results. We can see that SQL outperforms MLE+PG consistently across different diversity values. The outputs from MLE+PG are not diverse even with high ’s, indicating the model collapses and can only generate only a small set of unique adversarial examples. Table 1 shows generated samples by each method with highest entailment rate. The model by SQL discovers the pattern “saint-pierre-et-saint-paul” (an entity name), and exploits this to generate samples with high universal entailment rate.
4.3 Prompt Generation for Controlling Pretrained Language Models
The ability to optimize an arbitrary black-box reward has a broader implication. In particular, a reward function does not just have to be a metric like the BLEU score. It can also be a composition of multiple functions that eventually return a score.
To demonstrate this, we consider the task of prompting a large pretrained LM Radford et al. (2019); Brown et al. (2020) for controllable generation (Reiter and Dale, 1997; Hu et al., 2017). The goal is to learn to generate text prompts that steer the LM to generate sentences of certain desired attributes (e.g., topics). The problem of controlling the generation of pretrained LMs was previously approached through specialized algorithms such as modifying the LM hidden states during decoding Dathathri et al. (2020); Krause et al. (2020); Qin et al. (2020). Here we show that prompts offer an easier, faster, more effective way for controlled generation.
Learning to automatically generate or tune prompts is gaining increasing attention since the massive pretrained LMs (Brown et al., 2020). Most existing approaches Wallace et al. (2019); Li and Liang (2021); Lester et al. (2021)
rely on gradient backpropagation and are applicable only when the whole training pipeline is differentiable. The differentiability does not hold for the text generation setting, as illustrated in Figure5. In contrast, the RL framework is generally applicable to any differentiable or discrete pipelines.
Following (Dathathri et al., 2019), we aim to control the generation to have one of 7 topics (e.g., “science”); the generated prompt is prepended to one of 20 input sentences (Figure 5) for the pretrained LM to generate continuation sentences. There is no direct supervision data available for training the prompt generator. We randomly create some noisy text as the training data for MLE baselines below and for off-policy updates for our algorithm. Specifically, the noisy text is created by sampling keywords and topics from the list used in (Dathathri et al., 2020) and a paraphrase generation model.
Baselines and Setup.
Figure 5 shows the architecture of prompt-based controllable generation. We compare our SQL method with MLE+PG as before. At training time, for each generated prompt sample, the pretrained LM generates 2 continuation sentences for evaluating average reward. We use a zero-shot classifier to evaluate the topic accuracy of the continuation sentences (see appendix). That is, we do not assume access to classifiers pretrained on topic-specific sentences, because generating such topic-specific sentences is the goal of the task in the first place. We additionally use an LM to evaluate the log-likelihood of continuation sentences for measuring language quality. Since the prompt length could impact the generated sentences, we conducted experiments with maximum prompt length , , and . As ablation study, we also evaluate the SQL algorithm with only off-policy updates (i.e., without on-policy exploration), denoted as SQL (off), and compare it with vanilla MLE training. At test time, given a topic, the trained prompt generator produces one prompt using beam search decoding. For each generated prompt, the pretrained LM generates 100 sentences using top- decoding (with ) for evaluation. Finally, we also compare with two specialized controllable generation techniques based on pretrained LMs, namely PPLM Dathathri et al. (2019) and GeDi Krause et al. (2020)
, following similar procedures using their open-sourced code. We use a distilled GPT-2 model333https://huggingface.co/distilgpt2 as the pretrained LM to be controlled.
Figure 7 shows the topic accuracy of the controlled LM outputs averaged across the 7 topics, and Table 2 shows the respective language quality results. More detailed topic accuracy results are provided in the appendix (where GeDi obtained low accuracy on 2 of the 7 topics, possibly because the topic tokens are tokenized into two subwords for which the model released by the authors was not specifically trained). We can see that the prompts generated by our SQL cause the LM to generate sentences with high topic accuracy while maintaining low perplexity in most settings. Increasing the prompt length positively impacts the topic accuracy, which makes sense because longer prompts give more flexible for steering the LM. The comparison between MLE and SQL (off) shows that the off-policy component of SQL is better than standard MLE training, as it incorporates reward signals instead of just blindly following the (noisy) data.
Finally, comparing with the previous steered decoding such as PPLM and GeDi shows the advantage of the prompt-based control trained with RL. We can see the latter achieves better trade-off between topic accuracy and language quality. Moreover, once a prompt is produced, we can use the pretrained LM to generate text of desired topics efficiently, with the same time cost as standard non-controlled decoding. In comparison, the dedicated steered decoding is often orders-of-magnitude slower, as shown in Table 7.
4.4 Supervised Text Generation Tasks
Finally, we conduct experiment on standard generation tasks where clean supervised data is available. The study is to examine the capabilities of the proposed RL method to train a text generation model from scratch, which has been considered as exceedingly challenging for previous RL algorithms.
Datasets, Baselines, and Setup.
We study on two tasks, E2E (Novikova et al., 2017) and CommonGEN (Lin et al., 2020), and use the respective datasets pre-processed by Gehrmann et al. (2021) which allow sequence-to-sequence modeling with standard transformers. We run four sets of methods: the standard MLE training (MLE); PG training from scratch (PG); Joint MLE and PG training, with MLE initialization (MLE+PG); and our SQL training from scratch with both off-policy and on-policy updates (SQL
). We use the standard BLEU as reward. We additionally investigate the training stability and sensitivity w.r.t hyperparameters, in particular the scale of reward. To this end, forMLE+PG and SQL, we vary the reward scale in and evaluate the respective performance under different scales.
Table 3 shows the performance on E2E of different models whose hyperparameters are picked using the validation set. We can see the proposed SQL that trains models from scratch achieves competitive results with the common MLE and MLE+PG. In contrast, the PG algorithm alone without MLE fails the training. Figure 8 (left) shows the respective training curves (on the validation set), demonstrating that SQL converges in an efficient and stable way as MLE.
We further demonstrate the sensitive of MLE+PG and SQL w.r.t the reward scale as a key hyperparameter. Figure 8 (middle and right) shows the training curves of the two methods with varying reward scales. We can see SQL is significantly more robust as reward scale changes, while MLE+PG tends to collapse with improper reward scale configurations.
5 Related Work
Standard RL algorithms, such as -learning Sutton and Barto (2018), aim to find the best way to solve a given task. But, sometimes, the training can be over-sensitive to the randomness in the environment. Recent works have considered maximum-entropy RL (MaxEnt RL) extensions. MaxEnt RL optimizes policies to maximize both the expected reward as well as the expected entropy of the policy. Previous work in robotic and game control demonstrated that this formulation provides a substantial improvement in exploration and robustness Ziebart et al. (2008); Todorov (2008); Toussaint (2009); Ziebart (2010); Rawlik et al. (2013); O’Donoghue et al. (2017); Haarnoja et al. (2017); Nachum et al. (2017); Schulman et al. (2017); Nachum et al. (2018); Eysenbach and Levine (2021). One of the most prominent examples is soft -learning (SQL) Haarnoja et al. (2017); Nachum et al. (2017); Schulman et al. (2017), which modifies the -learning object to optimize not only the total reward but also the entropy (diversity) of the induced policy. In this work, we leverage one of the latest advances related to SQL, namely the path consistency learning Nachum et al. (2017).
Applying RL for text generation has been discussed with the goals of alleviating the exposure bias problem and directly optimizing task metrics (Ranzato et al., 2015; Li et al., 2016; Wu et al., 2016; Rennie et al., 2017; Paulus et al., 2018; Chen and Bansal, 2018). For example, Ranzato et al. (2015) used the REINFORCE algorithm Williams (1992), and Bahdanau et al. (2016) used the actor-critic algorithm. They are both on-policy RL algorithms with the need of pretraining their models using MLE. Cold-start Ding and Soricut (2017) proposed softmax policy gradient (SPG) that does not rely on MLE pretraining but requires various dedicated techniques for effective training (e.g., token-level decomposition of sequence reward, etc). Tan et al. (2018) proposed an entropy-regularized policy optimization (ERPO) formulation that subsumes many of the previous text generation training algorithms, ranging from MLE to Cold-start, as special cases. Our proposed framework offers solutions for efficient training from scratch in the presence of large action space and sparse sequence-level reward in text generation.
We have developed a new RL formulation for text generation based on soft -learning and path consistency learning. The proposed method combines off- and on-policy updates, and uses multi-step return to alleviate the issues with sparse sequence-level rewards. We conduct experiments and show impressive performance on four sets of experiments covering a wide range of applications: learning with noisy and negative data, black box adversarial attack, prompting a pretrained language model for controllable generation, and finally, on standard supervised tasks. The RL formulation opens up enormous new opportunities to integrate more advances made in the fertile RL literature to improve text and other sequence generation problems.
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Appendix A Appendix
a.1 Setup Details
We use four datasets: E2E [Novikova et al., 2017, Dušek et al., 2019], CommonGen [Lin et al., 2020], SNLI [Bowman et al., 2015], and MultiNLI [Williams et al., 2018]. Our evaluation follows the GEM Benchmark Gehrmann et al.  when applicable,444https://github.com/GEM-benchmark/GEM-metrics and otherwise same with the reward function used in training. SNLI is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Please see details in Williams et al.  for MultiNLI licensing Information, and https://gem-benchmark.com/data_cards/E2E and https://gem-benchmark.com/data_cards/CommonGen for E2E and CommonGen licensing Information.
We use the robust entailment classifier [Nie et al., 2020] in §4.1,555https://huggingface.co/ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli one of the most used entailment classifiers on HuggingFaceHub in §4.2.666https://github.com/pytorch/fairseq/tree/master/examples/roberta. This classifier is ranked #1 (as of May 20, 2021) based on https://huggingface.co/models?search=nli. and a zero-shot classifier based on BART Lewis et al.  to compute the topic score in §4.3.777https://huggingface.co/facebook/bart-large-mnli To compute perplexities, we use a GPT-2 model Radford et al.  fine-tuned on the corresponding datasets for computing perplexity in §4.1 and 4.2, and a distilled GPT-2 model in §4.3 without fine-tuning.888https://huggingface.co/distilgpt2 We simply set reward weights to , except in §4.2, where we changed the entailment weight to , log-likelihood and repetition penalty weight to .
For experiments that involve policy gradient training, we initialize the model with maximum likelihood training by default unless specified otherwise. We train soft -learning model from scratch with both off-policy (using data) and on-policy (using samples) by default except in §4.1 and 4.3, in which we find it beneficial to warm-up the model with just off-policy training. We apply similar tuning budgets to both soft -learning model, and policy-gradient (mostly the reward scale and top-), based on performance on the validation dataset and sample qualities.
For top- sampling results, we sample a hypothesis for each premise and measure the average attack rate across the dataset. This is because sampling multiple hypotheses, each for all premises, and measure performance are expensive. Since the hypotheses are sampled input-independently, this should be a good approximation.
We use the paraphrase generation model based on Zhang et al. .999https://huggingface.co/tuner007/pegasus_paraphrase During decoding, we include no_repeat_ngram_size, which improves readability.101010https://huggingface.co/blog/how-to-generate
a.2 Experimental Results
Please see Table 4 for beam search results.
|Model||Entl. Prob||Entl. Rate||PPL|
|GOLD-s Pang and He ||/||/||/||/||/|
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|religion: In summary nice things about Android 6.1 Jelly Bean!\n Searching for OP lag fixes one of my cllcs or some other improvements that’s fixing a bug due to this nerf! (model causing Huge Frame Decay!) It also fixed an upper turret hook|
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space: In summary
we have some important news from the moment of the year and some important information about these two major planets. This new discovery is the first to confirm this important planet has an active life in its home planet, a planet with a mass of about 5.8 billion tons. It
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|military: In summary we have some important news from the moment of the year and some important information from the moment of the year.\n\n\n\n\n We’ve also added an additional update for our new feature, which includes:\n • Improved access and access in all of the main|
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|computers: This essay discusses how you can build a new browser to view and share your favorite web sites.\n\n\n A browser that is open source can also be built from a web browser, which can be a browser that does not allow browser extensions (e.g. Firefox, Chrome, Opera|
|space: This essay discusses how you can build a life with a healthy diet and how you can use it when you’re ready to move forward. It’s a very simple approach to building a life with a healthy diet and what it means to be healthy and healthy for the|
|religion: This essay discusses how you can build a new game without having to play the original game, and how you can make a new title that is completely different to the original. It has been around since 2007, when the first game, The Elder Scrolls IV: Oblivion, was released in the PlayStation|
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