Paper: Unlocking Anticipatory Text Generation: A Constrained Approach for Faithful Decoding with Large Language Models (ICLR-2024)

Reviewer comment (Evaluation):
The satisfaction score estimation is currently limited and may not be robust or accurate enough for all constraints. More investigation into refining this estimation would be beneficial.

Author response:
***“The satisfaction score estimation is currently limited.”***

In this work, we use a scoring mechanism to identify if a constraint has not been satisfied yet to guide the future generation for LLMs. Our work highlights the demonstrated effectiveness of the current constraint score function. Additionally, the appendix, particularly in Figure 5, presents supplementary results using the score function with binary Yes/No prompts. While acknowledging the potential exploration of other scoring functions, we posit that tuned models or the utilization of smaller models with more robust or accurate future satisfaction score functions may prove more beneficial.

Reviewer comment (Experiments):
It is not clear if the gains will sustain for very long text generation where error accumulation could occur. More analysis on larger generation tasks could help.

Author response:
***“not clear if the gains will sustain for very long text generation.”***

We present the model outputs of different decoding methods for the long-form QA task in Table 10. Notably, there is a reduction in error accumulation, with a significant decrease in the number of typos for the word '401k.' Additionally, the human evaluation results in Table 7 further underscore the improvements across three dimensions: Fluency, Informativeness, and Correctness.

Reviewer comment (Evaluation):
There is no human evaluation of the quality and naturalness of outputs. Automatic metrics have limitations.

Author response:
***“Automatic metrics is limited."***

In our study, we surpass traditional n-gram-based evaluation metrics by integrating constrained satisfaction measures. These include the Coverage score for CommonGen, toxicity score from the Perspective API for the toxicity task, and correctness for the ALCE benchmark. Moreover, our research extends to human evaluations across three dimensions: Fluency, Informativeness, and Correctness. The combined outcomes from these diverse evaluations consistently emphasize the benefits of constraint satisfaction, ultimately leading to faithful generation.

Reviewer comment (Evaluation):
The factual correctness results are quite noisy and could benefit from more tuning and robustness testing.

Author response:
***“The factual correctness results are quite noisy”***

Significantly, we observed that answers are truncated by the first newline in the ALCE's script, impacting our findings. Consequently, we present updated results on faithful question-answering generation. The new results indicate the superiority of our proposed method.

Reviewer comment (Theory):
The definition of future Constraint Satisfaction is somewhat ambiguous to me. According to the formula at the bottom of page 2, your $R(y_{\leq t}, C(x))$ is used to approximate $\log p(C(x)|y _{\leq t})$, yet this is similar to the definition of $R$ provided in formula 1 on page 3. Could you please elaborate on the benefits of such a definition and why |SEP| token is added? This aspect lacks a comprehensive analysis.

Author response:
***“why |SEP| token is added?”***

When calculating the future constraint satisfaction score R with the given prefix $y_{<=t}$​ , the use of <SEP> is employed to delineate the prefix from the language description constraint C. This separation is crucial; otherwise, the logistic score on the first token of C conditioned on the prefix $y_{<=t}$  lacks meaningful interpretation. Our preliminary experiments indicate that this setting is more effective. Further details will be expounded upon in the upcoming version.

Reviewer comment (Novelty):
The approach considers the impact of prompt and prefix on the next token during the generation stage, with calculations only utilizing maximum likelihood estimation. The essence of future Constraint Satisfaction appears to revolve around the next token’s compatibility with the constraint. This similar idea is reflected in many controllable text generation methods, and the authors does not specifically compare these differences (only the different forms of control are mentioned in the related work section), which makes the paper seem like an incremental contribution.

Author response:
***“differences forms of work”***

Indeed, there is other related work on guided generation, but the forms of control differ significantly. In our approach, we verbalize the constraint and estimate future satisfaction scores using LLMs. Our motivation stems from our belief in the expressive power of language. Several distinctions set our work apart from previous endeavors. Firstly, we do not require additional fine-tuning for tasks. Secondly, our guidance is integrated into the token-level generation process as opposed to post-generation refinement. Thirdly, we employ natural language constraints, providing flexibility to incorporate various constraints. Lastly, our approach includes self-interpretation of these partial prefix outputs, among other unique features.

Reviewer comment (Experiments):
While the proposed method is relatively straightforward, it lacks robust experimental evidence to highlight its superiority over conventional decoding strategies. Moreover, the existing experimental results suggest that the improvements are rather limited.

Author response:
***“robust experimental evidence, one would expect more significant improvements.”***

We have achieved notable results, particularly in the context of CommonGen, with substantial improvements in coverage scores (e.g., Falcon-7B-Instruct: 88.7% -> 93.3%, LLaMa-2-13B-Chat: 93.6% -> 95.2%, Falcon-40B-Instruct: 88.7% -> 97.6%). Furthermore, we provide updated results on QA faithful generation on LLaMa-2-13B-Chat, addressing the issue of answer truncation caused by the first newline in the ALCE’s script. In addition to quantitative outcomes, human evaluations confirm superior improvements across three dimensions: Fluency, Informativeness, and Correctness. We believe that future gains can be achieved with a more robust or accurate future-constrained score, potentially through the refinement or tuning of models.

Reviewer comment (Evaluation):
While the method has been empirically shown to better perform than baselines in terms of n-gram overlap and correctness, there are other dimensions that are not reported. Firstly, since the method essentially introduces a call to a language model for each beam and for each timestep in the beam search, we expect that the decoding time is slow. How much is the tradeoff between this and "text quality"?

Author response:
***“Decoding time, tradeoff between time complexity and generated text quality ”***

Our method incurs a linear slowdown of approximately $k$ times, primarily attributable to the overhead associated with computing future satisfaction scores on candidates. A detailed breakdown of decoding times for each example in our experiments is provided in Table 6.

It is worthwhile to note that the increase in decoding time is a reasonable trade-off for achieving faithful generation. To mitigate this, several strategies can be employed to maintain generation quality while reducing time, such as selecting a smaller $k$ and opting for smaller yet finely tuned LLMs capable of efficiently computing the future constraint satisfaction score $R(\y_{<=t}, C(\vx))$.

Reviewer comment (Evaluation):
Since the focus is on "faithful decoding", the paper should have focused on those evals as well (and not on metrics based on n-gram overlap).

Author response:
***“Evaluation metrics”***

We not only present evaluation metrics based on n-grams but also include constrained satisfaction scores, such as the Coverage score for CommonGen, toxicity score from Perspective API for the toxicity task, and correctness for the ALCE benchmark. Additionally, illustrative outputs are showcased in Table 9 and Table 10. Our study encompasses human evaluations across three dimensions: Fluency, Informativeness, and Correctness. Collectively, these findings highlight the benefits of constraint satisfaction, contributing to faithful generation.

Reviewer comment (Experiments):
Parts of the paper are difficult to understand. For example, since there is no mention of how the experiments are set up, it was very difficult to comprehend what the authors wanted to convey in Figure 2 since it showed a bunch of previously unintroduced models and mentioned terms not defined (e.g., Ranking accuracy). This issue is repeated in Figures 4 and 5. I think overall the paper needs proofreading.

Author response:
***“Lack experiments setup details”***

We update the experiment setup and add more details on these models in Section A.1.

Reviewer comment (Evaluation):
What is the tradeoff between time complexity and generated text quality?

Author response:
***“Decoding time, tradeoff between time complexity and generated text quality ”***

Our method incurs a linear slowdown of approximately $k$ times, primarily attributable to the overhead associated with computing future satisfaction scores on candidates. A detailed breakdown of decoding times for each example in our experiments is provided in Table 6.

It is worthwhile to note that the increase in decoding time is a reasonable trade-off for achieving faithful generation. To mitigate this, several strategies can be employed to maintain generation quality while reducing time, such as selecting a smaller $k$ and opting for smaller yet finely tuned LLMs capable of efficiently computing the future constraint satisfaction score $R(\y_{<=t}, C(\vx))$.

Reviewer comment (Novelty):
The novelty of the new constraint scoring function is fairly limited.

Author response:
In this work, we use a scoring mechanism to identify if a constraint has not been satisfied yet to guide the future generation for LLMs. Our work highlights the demonstrated effectiveness of the current constraint score function. Additionally, the appendix, particularly in Figure 5, presents supplementary results using the score function with binary Yes/No prompts. While acknowledging the potential exploration of other scoring functions, we posit that tuned models or the utilization of smaller models with more robust or accurate future satisfaction score functions may prove more beneficial.

Reviewer comment (Evaluation):
Overall performance gains are not large and only help small sized LLMs.

Author response:
We have achieved notable results, particularly in the context of CommonGen, with substantial improvements in coverage scores (e.g., Falcon-7B-Instruct: 88.7% -> 93.3%, LLaMa-2-13B-Chat: 93.6% -> 95.2%, Falcon-40B-Instruct: 88.7% -> 97.6%). Furthermore, we provide updated results on QA faithful generation on LLaMa-2-13B-Chat, addressing the issue of answer truncation caused by the first newline in the ALCE’s script. In addition to quantitative outcomes, human evaluations confirm superior improvements across three dimensions: Fluency, Informativeness, and Correctness. We believe that future gains can be achieved with a more robust or accurate future-constrained score, potentially through the refinement or tuning of models.

Reviewer comment (Reproducibility):
Does the <SEP> token seperate the prompt and continuation? Is it same across all the LLMs? Is it repurposed from one of the special tokens during pre-training?

Author response:
No, the <SEP> token is specifically employed when calculating the future constraint score to separate the prefix and verbalized constraint. It's important to note that there is no <SEP> token used to separate the prompt and continuation.

Reviewer comment (Evaluation):
Does the inference mechanism ever lead to degenerate sequences? If yes, how often does that occur?

Author response:
We did not observe any degenerate sequences. The hyperparameters $k$ and $\lambda$ in Formula (2) can be adjusted to control the generation behavior.