A computational argumentation framework evaluates LLM summaries of parliamentary debates by checking preservation of formal argument structures tied to contested proposals.
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Pith papers citing it
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HORST uses non-commutative operator composition and a hyperbolic mirror map to combine stability from adaptive optimizers with L1 sparsity bias, outperforming AdamW across sparsity levels on vision and language tasks.
Introduces the CHA-Gen dataset for Chinese ambiguity based on Potential Ambiguity Theory and shows LLMs struggle to detect ambiguity, exhibiting specific failure modes and overconfidence after instruction tuning.
A dataset-agnostic framework converts text tool-calling benchmarks to paired audio evaluations via TTS, speaker variation and noise, then evaluates seven omni-modal models showing model- and task-dependent performance with small text-to-voice gaps.
R³AG routes queries to retrievers by decomposing capabilities into retrieval quality and generation utility, trained via contrastive learning on document assessments and downstream answer correctness to outperform static methods.
LLMs show strong position bias when scoring model outputs, allowing easy manipulation of rankings, but calibration with multiple evidence, position balancing, and selective human input reduces this bias to better match human judgments.
ModernBERT is a new bidirectional encoder model achieving SOTA performance on diverse classification and retrieval benchmarks while offering superior speed and memory efficiency for long-context inference.
InternLM2 is a new open-source LLM that outperforms prior versions on 30 benchmarks and long-context tasks through scaled pre-training to 32k tokens and a conditional online RLHF alignment strategy.
DeepSeekMoE 2B matches GShard 2.9B performance and approaches a dense 2B model; the 16B version matches LLaMA2-7B at 40% compute by using fine-grained expert segmentation plus shared experts.
citing papers explorer
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Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation
A computational argumentation framework evaluates LLM summaries of parliamentary debates by checking preservation of formal argument structures tied to contested proposals.
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HORST: Composing Optimizer Geometries for Sparse Transformer Training
HORST uses non-commutative operator composition and a hyperbolic mirror map to combine stability from adaptive optimizers with L1 sparsity bias, outperforming AdamW across sparsity levels on vision and language tasks.
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Evaluating Chinese Ambiguity Understanding in Large Language Models
Introduces the CHA-Gen dataset for Chinese ambiguity based on Potential Ambiguity Theory and shows LLMs struggle to detect ambiguity, exhibiting specific failure modes and overconfidence after instruction tuning.
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From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents
A dataset-agnostic framework converts text tool-calling benchmarks to paired audio evaluations via TTS, speaker variation and noise, then evaluates seven omni-modal models showing model- and task-dependent performance with small text-to-voice gaps.
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R$^3$AG: Retriever Routing for Retrieval-Augmented Generation
R³AG routes queries to retrievers by decomposing capabilities into retrieval quality and generation utility, trained via contrastive learning on document assessments and downstream answer correctness to outperform static methods.
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Large Language Models are not Fair Evaluators
LLMs show strong position bias when scoring model outputs, allowing easy manipulation of rankings, but calibration with multiple evidence, position balancing, and selective human input reduces this bias to better match human judgments.
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Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
ModernBERT is a new bidirectional encoder model achieving SOTA performance on diverse classification and retrieval benchmarks while offering superior speed and memory efficiency for long-context inference.
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InternLM2 Technical Report
InternLM2 is a new open-source LLM that outperforms prior versions on 30 benchmarks and long-context tasks through scaled pre-training to 32k tokens and a conditional online RLHF alignment strategy.
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DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
DeepSeekMoE 2B matches GShard 2.9B performance and approaches a dense 2B model; the 16B version matches LLaMA2-7B at 40% compute by using fine-grained expert segmentation plus shared experts.