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Answer Selection

Answer Selection is the task of identifying the correct answer to a question from a pool of candidate answers. This task can be formulated as a classification or a ranking problem.

Source: Learning Analogy-Preserving Sentence Embeddings for Answer Selection

Papers

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Showing 1–25 of 171 papers

TitleStatusHype
FinBERT-QA: Financial Question Answering with pre-trained BERT Language ModelsCode2
Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarksCode2
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language ModelsCode1
Leveraging Large Language Models for Multiple Choice Question AnsweringCode1
Paragraph-based Transformer Pre-training for Multi-Sentence InferenceCode1
CICERO: A Dataset for Contextualized Commonsense Inference in DialoguesCode1
[Re] Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base EmbeddingsCode1
ComQA:Compositional Question Answering via Hierarchical Graph Neural NetworksCode1
Utilizing Bidirectional Encoder Representations from Transformers for Answer SelectionCode1
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive ScaleCode1
Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base EmbeddingsCode1
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection TaskCode1
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic ClusteringCode1
Could Thinking Multilingually Empower LLM Reasoning?Code0
Enhancing Mathematical Reasoning in Large Language Models with Self-Consistency-Based Hallucination Detection—0
Evaluating Answer Reranking Strategies in Time-sensitive Question Answering—0
FANS -- Formal Answer Selection for Natural Language Math Reasoning Using Lean4—0
SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQACode0
Zero-Shot End-To-End Spoken Question Answering In Medical Domain—0
HGOT: Hierarchical Graph of Thoughts for Retrieval-Augmented In-Context Learning in Factuality EvaluationCode0
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model LeaderboardsCode0
Enhancing Answer Selection in Community Question Answering with Pre-trained and Large Language Models—0
Evaluating LLMs on Document-Based QA: Exact Answer Selection and Numerical Extraction using Cogtale dataset—0
Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering—0
SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References—0
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