Retrieving Contextual Information for Long-Form Question Answering using Weak Supervision (2410.08623v1)

Abstract: Long-form question answering (LFQA) aims at generating in-depth answers to end-user questions, providing relevant information beyond the direct answer. However, existing retrievers are typically optimized towards information that directly targets the question, missing out on such contextual information. Furthermore, there is a lack of training data for relevant context. To this end, we propose and compare different weak supervision techniques to optimize retrieval for contextual information. Experiments demonstrate improvements on the end-to-end QA performance on ASQA, a dataset for long-form question answering. Importantly, as more contextual information is retrieved, we improve the relevant page recall for LFQA by 14.7% and the groundedness of generated long-form answers by 12.5%. Finally, we show that long-form answers often anticipate likely follow-up questions, via experiments on a conversational QA dataset.

Retrieving Contextual Information for Long-Form Question Answering using Weak Supervision

The research paper titled "Retrieving Contextual Information for Long-Form Question Answering using Weak Supervision" employs innovative techniques to improve the retrieval and generation processes in long-form question answering (LFQA). The paper addresses a crucial gap in existing retrieval systems that tend to focus on direct answers, often omitting pertinent contextual information. This research introduces and evaluates weak supervision strategies to enhance the retrieval systems' capacity to provide comprehensive responses.

Core Contributions

The paper's foremost contribution is the development of a methodology for crafting "silver passages." These are sourced through a weak supervision approach, where both direct and long-form answers guide the selection of relevant passages. This method focuses on retrieving not only the factual answers but also sufficient context to bolster the generation of more grounded and extensive responses.

Additionally, the research delineates between LFQA and conversational question answering (ConvQA), positing them as complementary efforts in satisfying user information needs. By training BERT-based re-ranking models and leveraging a LLM, the researchers effectively boost the performance on the ASQA dataset, a standard LFQA benchmark. Notably, the paper reports a 14.7% improvement in relevant page recall and a 12.5% increase in answer groundedness.

Methodological Insights

The paper proposes several techniques for deriving silver passages, including lexical matching, semantic similarity measurement, and LLM perplexity analysis. These methodologies are compared against the backdrop of sole direct answer matching, revealing that combining direct answers with contextual long-form answer guidance offers superior results.

One standout finding is the strong performance of lexical matching, which surpasses more complex approaches in aligning relevant context with long-form answers. The proposed Silver mechanism, which simultaneously targets direct and contextual information, encapsulates the paper's innovative approach.

Results and Implications

Empirical results demonstrate the effectiveness of the proposed method, achieving state-of-the-art performance on the ASQA dataset. The retrieval systems’ evolution towards contextual information is corroborated by improvements seen in end-to-end QA metrics, particularly the DR metric.

The paper also highlights notable enhancements in groundedness, indicating a reduction in the hallucination of facts by the LLM. This aspect is crucial for the credibility and reliability of automated question-answering systems. Furthermore, experiments on ConvMix dataset showcase the method's generalizability to unseen conversational contexts, underscoring the robustness of the retrieval approach.

Future Directions

The paper opens avenues for further exploration in several domains. Potential extensions could involve applying this retrieval enhancement framework to other LLM architectures or exploring its versatility across various QA datasets. Another prospective investigation could involve real-world applications and validation in dynamic, user-driven environments.

In summary, this work underscores the significance of retrieving contextual information in LFQA, using weak supervision to bridge existing gaps in retrieval-augmented generation systems. The findings promise to influence ongoing and future research on improving the interplay between retrieval precision and answer generation, crucial for constructing knowledgeable and reliable AI systems in question answering.