Time-Reversal Provides Unsupervised Feedback to LLMs (2412.02626v3)

Abstract: LLMs are typically trained to predict in the forward direction of time. However, recent works have shown that prompting these models to look back and critique their own generations can produce useful feedback. Motivated by this, we explore the question of whether LLMs can be empowered to think (predict and score) backwards to provide unsupervised feedback that complements forward LLMs. Towards this, we introduce Time Reversed LLMs (TRLMs), which can score and generate queries when conditioned on responses, effectively functioning in the reverse direction of time. Further, to effectively infer in the response to query direction, we pre-train and fine-tune a LLM (TRLM-Ba) in the reverse token order from scratch. We show empirically (and theoretically in a stylized setting) that time-reversed models can indeed complement forward model predictions when used to score the query given response for re-ranking multiple forward generations. We obtain up to 5\% improvement on the widely used AlpacaEval Leaderboard over the competent baseline of best-of-N re-ranking using self log-perplexity scores. We further show that TRLM scoring outperforms conventional forward scoring of response given query, resulting in significant gains in applications such as citation generation and passage retrieval. We next leverage the generative ability of TRLM to augment or provide unsupervised feedback to input safety filters of LLMs, demonstrating a drastic reduction in false negative rate with negligible impact on false positive rates against several attacks published on the popular JailbreakBench leaderboard.

• The paper presents Time-Reversed Language Models (TRLMs) that flip the traditional prediction process to offer unsupervised feedback.
• It details two variants—TRLM-Ba and TRLM-Fo—and their combined model TRLM-FoBa, which improve reranking and citation attribution tasks.
• Empirical results include a 5% win rate boost in reranking and a 44.15% increase in citation accuracy, demonstrating practical improvements in NLP applications.

Time Reversal Provides Unsupervised Feedback to LLMs

The discussed paper presents a novel approach aimed at augmenting the functionality of LLMs by introducing Time-Reversed LLMs (TRLMs), which provide unsupervised feedback by predicting and scoring responses in a reversed temporal context. The concept of time-reversal in LLMing is relatively unexplored, and the authors propose TRLMs as a means to score and generate queries when conditioned on responses, an operation counter to the typical forward direction handling in LLMs.

Concept and Implementation

The primary innovation is the TRLM, which operates in the unconventional response-to-query direction, essentially flipping the traditional LLMing task. The paper explores two main variants of TRLMs:

1. TRLM-Ba: This model involves training LLMs from scratch in a reversed token order, allowing for natural backward predictions.
2. TRLM-Fo: This variant uses forward-trained LLMs but conditions them to score or generate in the reverse direction through strategic prompting.

Additionally, the authors propose a combined model, TRLM-FoBa, that encompasses both forward and reverse training. The reversed operation effectively encourages TRLMs to conditionally enhance feedback during tasks like re-ranking generated responses, improving citation generation accuracy, and strengthening retrieval processes.

Empirical and Theoretical Validation

Empirical evaluations demonstrated that TRLM-based models can outperform conventional forward models across several tasks, thereby underscoring their potential. For instance, in Best-of-N reranking on the AlpacaEval leaderboard, TRLM-Ba showed a 5% improvement in length-controlled win rates compared to existing Gemini-Pro-1.0 baseline models. These findings suggest that TRLMs provide valid and beneficial feedback, leading to better alignment of LLM generations without needing additional supervised data.

In citation attributing for the CNN Daily Mail dataset, TRLMs significantly improved performance metrics, such as a 44.15% increase in citation attribution accuracy. Furthermore, TRLMs demonstrated notable gains in passage retrieval tasks on datasets like MS-Marco and NF-Corpus, where reverse scoring evidently outperformed forward scoring baseline methods.

Theoretical aspects of the paper touch upon the alignment strengths of TRLM models using a formal model hypothesizing that TRLMs align distributions more tightly by narrowing down the support to the most relevant answers for given queries.

Practical Implications and Future Directions

TRLMs could transformative numerous NLP applications, particularly where response accuracy is critical, such as in automated customer support, educational tutoring systems, and legal document analysis. Beyond practical tasks, the theoretical framework suggests potential for further exploration in model alignment using reverse distributions to mitigate "hallucination" in LLM outputs.

Potential future research avenues might involve integrating TRLMs in larger-scale multi-task learning contexts or combining TRLMs with reinforcement learning paradigms to further refine model predictions in dynamic environments.

Conclusion

The introduction of Time-Reversed LLMs emerges as a promising direction for advancing LLM capabilities, providing unsupervised feedback that enhances current models' performance on various complex tasks. This work not only opens new possibilities within the field of LLMing but also sets a foundation for subsequent research on leveraging reverse conditioning as a strategic tool in machine learning and AI. The paper's results contribute significantly to ongoing discussions on unsupervised learning methods and align well with industry needs for robust, data-efficient models that can operate effectively with minimal supervision.