Context Filtering with Reward Modeling in Question Answering

• URL: http://arxiv.org/abs/2412.11707v1
• Date: Mon, 16 Dec 2024 12:29:24 GMT
• Title: Context Filtering with Reward Modeling in Question Answering
• Authors: Sangryul Kim, James Thorne,
• Abstract summary: We introduce a context filtering approach that removes non-essential details, summarizing crucial content through Reward Modeling. We show that our approach can significantly outperform the baseline, as evidenced by a 6.8-fold increase in the EM Per Token (EPT) metric.
• Score: 7.668954669688971
• License:
• Abstract: Question Answering (QA) in NLP is the task of finding answers to a query within a relevant context retrieved by a retrieval system. Yet, the mix of relevant and irrelevant information in these contexts can hinder performance enhancements in QA tasks. To address this, we introduce a context filtering approach that removes non-essential details, summarizing crucial content through Reward Modeling. This method emphasizes keeping vital data while omitting the extraneous during summarization model training. We offer a framework for developing efficient QA models by discerning useful information from dataset pairs, bypassing the need for costly human evaluation. Furthermore, we show that our approach can significantly outperform the baseline, as evidenced by a 6.8-fold increase in the EM Per Token (EPT) metric, which we propose as a measure of token efficiency, indicating a notable token-efficiency boost for low-resource settings.

Related papers

• Learning Task Representations from In-Context Learning [73.72066284711462]
  Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning. We introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads. We show that our method's effectiveness stems from aligning the distribution of the last hidden state with that of an optimally performing in-context-learned model.
  arXiv  Detail & Related papers  (2025-02-08T00:16:44Z)
• Context Awareness Gate For Retrieval Augmented Generation [2.749898166276854]
  Retrieval Augmented Generation (RAG) has emerged as a widely adopted approach to mitigate the limitations of large language models (LLMs) in answering domain-specific questions. Previous research has predominantly focused on improving the accuracy and quality of retrieved data chunks to enhance the overall performance of the generation pipeline. We investigate the impact of retrieving irrelevant information in open-domain question answering, highlighting its significant detrimental effect on the quality of LLM outputs.
  arXiv  Detail & Related papers  (2024-11-25T06:48:38Z)
• Enhancing Question Answering Precision with Optimized Vector Retrieval and Instructions [1.2425910171551517]
  Question-answering (QA) is an important application of Information Retrieval (IR) and language models. We propose an innovative approach to improve QA task performances by integrating optimized vector retrievals and instruction methodologies.
  arXiv  Detail & Related papers  (2024-11-01T21:14:04Z)
• Learning to Filter Context for Retrieval-Augmented Generation [75.18946584853316]
  Generation models are required to generate outputs given partially or entirely irrelevant passages. FILCO identifies useful context based on lexical and information-theoretic approaches. It trains context filtering models that can filter retrieved contexts at test time.
  arXiv  Detail & Related papers  (2023-11-14T18:41:54Z)
• Fine-tuning and aligning question answering models for complex information extraction tasks [0.8392546351624164]
  extractive language models like question answering (QA) or passage retrieval models guarantee query results to be found within the boundaries of an according context document. We show that fine-tuning existing German QA models boosts performance for tailored extraction tasks of complex linguistic features. We deduce a combined metric from Levenshtein distance, F1-Score, Exact Match and ROUGE-L to mimic the assessment criteria from human experts.
  arXiv  Detail & Related papers  (2023-09-26T10:02:21Z)
• QontSum: On Contrasting Salient Content for Query-focused Summarization [22.738731393540633]
  Query-focused summarization (QFS) is a challenging task in natural language processing that generates summaries to address specific queries. This paper highlights the role of QFS in Grounded Answer Generation (GAR) We propose QontSum, a novel approach for QFS that leverages contrastive learning to help the model attend to the most relevant regions of the input document.
  arXiv  Detail & Related papers  (2023-07-14T19:25:35Z)
• Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks [54.306234256074255]
  We identify the issue of tokenization inconsistency that is commonly neglected in training generative models. This issue damages the extractive nature of these tasks after the input and output are tokenized inconsistently. We show that, with consistent tokenization, the model performs better in both in-domain and out-of-domain datasets.
  arXiv  Detail & Related papers  (2022-12-19T23:33:21Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance [43.820971952979875]
  We propose QFS-BART, a model that incorporates the explicit answer relevance of the source documents given the query via a question answering model. Our model can take advantage of large pre-trained models which improve the summarization performance significantly. Empirical results on the Debatepedia dataset show that the proposed model achieves the new state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-27T06:58:42Z)
• Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary [65.37544133256499]
  We propose a metric to evaluate the content quality of a summary using question-answering (QA) We demonstrate the experimental benefits of QA-based metrics through an analysis of our proposed metric, QAEval.
  arXiv  Detail & Related papers  (2020-10-01T15:33:09Z)
• Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering [98.48363619128108]
  We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance.
  arXiv  Detail & Related papers  (2020-04-24T17:57:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.