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Showing 51–100 of 124 papers

TitleStatusHype
From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic DataCode0
Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-JudgesCode0
CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAGCode0
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented GenerationCode0
LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language ModelsCode0
Accurate and Nuanced Open-QA Evaluation Through Textual EntailmentCode0
KS-LLM: Knowledge Selection of Large Language Models with Evidence Document for Question Answering—0
Mitigating LLM Hallucinations via Conformal Abstention—0
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction—0
Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents—0
Fine-Grained Self-Endorsement Improves Factuality and Reasoning—0
The Generative AI Paradox on Evaluation: What It Can Solve, It May Not Evaluate—0
Attendre: Wait To Attend By Retrieval With Evicted Queries in Memory-Based Transformers for Long Context Processing—0
Efficient Transformer Knowledge Distillation: A Performance Review—0
Noisy Pair Corrector for Dense Retrieval—0
A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative ModelsCode0
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference—0
When to Read Documents or QA History: On Unified and Selective Open-domain QA—0
RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question AnsweringCode0
Allies: Prompting Large Language Model with Beam SearchCode0
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human FeedbackCode0
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive CritiquingCode0
Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders—0
Dense Sparse Retrieval: Using Sparse Language Models for Inference Efficient Dense Retrieval—0
CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models—0
Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?—0
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering—0
Unsupervised Question Answering via Answer DiversifyingCode0
SKILL: Structured Knowledge Infusion for Large Language Models—0
A Copy-Augmented Generative Model for Open-Domain Question Answering—0
GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval—0
Deep Understanding based Multi-Document Machine Reading Comprehension—0
Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation—0
You Only Need One Model for Open-domain Question Answering—0
What’s in a Name? Answer Equivalence For Open-Domain Question AnsweringCode0
Challenges in Generalization in Open Domain Question Answering—0
A Copy-Augmented Generative Model for Open-Domain Question Answering—0
Attention-guided Generative Models for Extractive Question Answering—0
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering—0
Adversarial Retriever-Ranker for dense text retrievalCode0
Relation-Guided Pre-Training for Open-Domain Question Answering—0
Improving Unsupervised Question Answering via Summarization-Informed Question Generation—0
What's in a Name? Answer Equivalence For Open-Domain Question AnsweringCode0
Challenges in Generalization in Open Domain Question AnsweringCode0
RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering—0
On the Influence of Masking Policies in Intermediate Pre-training—0
Evaluating Neural Model Robustness for Machine Comprehension—0
Machine Reading Comprehension with Enhanced Linguistic Verifiers—0
UnitedQA: A Hybrid Approach for Open Domain Question Answering—0
Studying Strategically: Learning to Mask for Closed-book QA—0
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