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Showing 76–100 of 124 papers

TitleStatusHype
RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering—0
Relation-Guided Pre-Training for Open-Domain Question Answering—0
Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents—0
Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation—0
Self-Training Large Language Models for Tool-Use Without Demonstrations—0
SFR-RAG: Towards Contextually Faithful LLMs—0
ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices—0
Simple and Effective Semi-Supervised Question Answering—0
SKILL: Structured Knowledge Infusion for Large Language Models—0
Smarnet: Teaching Machines to Read and Comprehend Like Human—0
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference—0
Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting—0
Studying Strategically: Learning to Mask for Closed-book QA—0
The Generative AI Paradox on Evaluation: What It Can Solve, It May Not Evaluate—0
Tradeoffs in Sentence Selection Techniques for Open-Domain Question Answering—0
UnitedQA: A Hybrid Approach for Open Domain Question Answering—0
Vision-centric Token Compression in Large Language Model—0
When to Read Documents or QA History: On Unified and Selective Open-domain QA—0
Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models—0
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity—0
LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language ModelsCode0
KV Prediction for Improved Time to First TokenCode0
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented GenerationCode0
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human FeedbackCode0
Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-JudgesCode0
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