Hindsight: Posterior-guided training of retrievers for improved open-ended generation (2110.07752v2)

Abstract: Many text generation systems benefit from using a retriever to retrieve passages from a textual knowledge corpus (e.g., Wikipedia) which are then provided as additional context to the generator. For open-ended generation tasks (like generating informative utterances in conversations) many varied passages may be equally relevant and we find that existing methods that jointly train the retriever and generator underperform: the retriever may not find relevant passages even amongst the top-10 and hence the generator may not learn a preference to ground its generated output in them. We propose using an additional guide retriever that is allowed to use the target output and "in hindsight" retrieve relevant passages during training. We model the guide retriever after the posterior distribution Q of passages given the input and the target output and train it jointly with the standard retriever and the generator by maximizing the evidence lower bound (ELBo) in expectation over Q. For informative conversations from the Wizard of Wikipedia dataset, with posterior-guided training, the retriever finds passages with higher relevance in the top-10 (23% relative improvement), the generator's responses are more grounded in the retrieved passage (19% relative improvement) and the end-to-end system produces better overall output (6.4% relative improvement).

Analyzing "Hindsight: Posterior-guided training of retrievers for improved open-ended generation"

The paper "Hindsight: Posterior-guided training of retrievers for improved open-ended generation" proposes an innovative approach for augmenting open-ended text generation tasks with relevant retrieval-based context. In traditional systems for such tasks, a retriever mechanism is utilized to obtain passages from a corpus like Wikipedia, which are then provided as supplementary context to generation models. These systems, however, encounter difficulties when attempting to retrieve contextually appropriate passages—especially in cases that involve multiple plausible outputs or responses. This paper introduces a novel method by employing a guide retriever modeled after the posterior distribution, which refines the selection of relevant passages during the training process.

Key Contributions

1. Posterior-Guided Training: The central contribution of the paper is the concept of posterior-guided training for retrievers. In this approach, a guide retriever that can access target outputs evaluates relevance in hindsight. This retriever simulates the posterior probability distribution $Q(z|x, y)$ of passages given both the input $x$ and the target output $y$, creating a more accurate supervision signal during training by optimizing the Evidence Lower Bound (ELBo).
2. Empirical Validation: The paper provides empirical evidence that this posterior-guided training method improves retrieval relevance significantly—23% improvement in finding relevant passages within the top-10 results and enhances grounding of generated responses by 19%, as demonstrated in the Wizard of Wikipedia dataset.
3. Iterative Closed-Set Training: The authors outline an iterative training strategy that involves creating an initial high-recall subset of passages from the corpus, which allows efficient inner-loop training. This iterative process results in a considerable boost in retrieving relevant passages.

Numerical Findings

The results are robust across various metrics and settings. In the Wizard of Wikipedia dataset, the posterior-guided retrievers achieved a success@10 rate of 63.9%, outperforming the marginalized loss retriever (52.8%). Similarly, the grounding score, measured via Novel-F1, was markedly higher. The Hindsight system further distinguished itself through substantial improvements in one-to-one tasks using the MS-MARCO NLGen dataset, albeit with reduced yet meaningful increments. These metrics highlight how the proposed methodology refines retrieval and improves the quality of generated outputs for open-ended tasks.

Implications and Future Directions

This posterior-guided retriever training proposes substantial implications for models requiring precise grounding in vast data corpora. The improved ability to retrieve contextually relevant documents advances the quality of responses generated by LLMs in applications ranging from conversational agents to elaborate informative generation tasks. The research insinuates a potential transformative effect on AI systems that require integrating dynamic knowledge bases, pointing towards future work that could further explore the synergy between retentive and generative components in AI, benefiting areas such as interactive tasks and personalized content delivery.

In conclusion, the introduction of posterior-guided retrievers empowers end-to-end retrieval-augmented generation systems to more effectively harness large text corpora, cultivating a grounded, contextually relevant generation that surpasses traditional methods in both efficacy and flexibility. It sets the stage for further exploration into refining generative models for complex, real-world interactions.