LCDD creates sparse carriers for SFT behaviors that SFT-Eraser can reverse, with ablations showing the sparse structure enables causal control.
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We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B -- Instruct, that surpasses the Llama 2 13B -- Chat model both on human and automated benchmarks. Our models are released under the Apache 2.0 license.
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- abstract We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B -- Instruct, that surpasses the Llama 2 13B -- Chat model both on human and auto
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citing papers explorer
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Large Language Diffusion Models
LLaDA is a scalable diffusion-based language model that matches autoregressive LLMs like LLaMA3 8B on tasks and surpasses GPT-4o on reversal poem completion.
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Evaluating Very Long-Term Conversational Memory of LLM Agents
Creates LoCoMo benchmark dataset for very long-term LLM conversational memory and shows current models struggle with lengthy dialogues and long-range temporal dynamics.
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Inducing Artificial Uncertainty in Language Models
Inducing artificial uncertainty on trivial tasks allows training probes that achieve higher calibration on hard data than standard approaches while retaining performance on easy data.
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TokAlign++: Advancing Vocabulary Adaptation via Better Token Alignment
TokAlign++ learns token alignments between LLM vocabularies from monolingual representations to enable faster adaptation, better text compression, and effective token-level distillation across 15 languages with minimal steps.
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VITA-QinYu: Expressive Spoken Language Model for Role-Playing and Singing
VITA-QinYu is the first expressive end-to-end spoken language model supporting role-playing and singing alongside conversation, trained on 15.8K hours of data and outperforming prior models on expressiveness and conversational benchmarks.
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The First Token Knows: Single-Decode Confidence for Hallucination Detection
First-token normalized entropy (phi_first) from one greedy decode reaches mean AUROC 0.820 for hallucination detection, matching or exceeding semantic self-consistency (0.793) and surface self-consistency (0.791) across three 7-8B models and two benchmarks.
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Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders
EPIC trains LLMs to treat continuous embeddings as in-context prompts, yielding state-of-the-art text embedding performance on MTEB with or without prompts at inference and lower compute.
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A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis
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ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?
Personalized LLM-generated plain language summaries improve lay readers' comprehension and quality ratings but increase risks of reinforcing biases and introducing hallucinations compared to static expert summaries.
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Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective
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Evaluating Temporal Consistency in Multi-Turn Language Models
Language models frequently violate temporal scope stability in multi-turn dialogues by drifting toward present-day assumptions even when they possess the correct facts.
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Serialisation Strategy Matters: How FHIR Data Format Affects LLM Medication Reconciliation
Clinical narrative format beats raw JSON for LLMs up to 8B parameters on medication reconciliation but raw JSON wins at 70B scale, with omissions as the main error type.
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How Tokenization Limits Phonological Knowledge Representation in Language Models and How to Improve Them
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RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
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GTA-2: Benchmarking General Tool Agents from Atomic Tool-Use to Open-Ended Workflows
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Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size
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Transactional Attention: Semantic Sponsorship for KV-Cache Retention
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Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models
Agreeableness in AI personas reliably predicts sycophantic behavior in 9 of 13 tested language models.
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TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice
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TriAttention: Efficient Long Reasoning with Trigonometric KV Compression
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Is a Picture Worth a Thousand Words? Adaptive Multimodal Fact-Checking with Visual Evidence Necessity
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DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
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Jamba: A Hybrid Transformer-Mamba Language Model
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Output Composability of QLoRA PEFT Modules for Plug-and-Play Attribute-Controlled Text Generation
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LLM-Agnostic Semantic Representation Attack
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ReST-KV: Robust KV Cache Eviction with Layer-wise Output Reconstruction and Spatial-Temporal Smoothing
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Reformulating KV Cache Eviction Problem for Long-Context LLM Inference
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UniPrefill: Universal Long-Context Prefill Acceleration via Block-wise Dynamic Sparsification
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More Aligned, Less Diverse? Analyzing the Grammar and Lexicon of Two Generations of LLMs
Newer LLMs exhibit reduced syntactic and lexical diversity in English news text generation compared to older models, as measured by HPSG grammar and diversity metrics from ecology and information theory, while human-authored text shows little change.
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Hallucination as an Anomaly: Dynamic Intervention via Probabilistic Circuits
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Learn-to-learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM
A hypernetwork generates meta-gating parameters for SwiGLU blocks to let LLMs adapt their nonlinearity to arbitrary textual conditions, outperforming finetuning and meta-learning baselines with reasonable generalization to unseen cases.
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Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus
Machine translation preserves embedding similarity structure for ten languages but distorts it for four in the Manifesto Corpus, via a new non-inferiority testing framework.
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From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization
LLM 2-bit quantization fails via either cumulative signal degradation or early computation collapse in key components.
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Mind the Unseen Mass: Unmasking LLM Hallucinations via Soft-Hybrid Alphabet Estimation
SHADE adaptively combines coverage and spectral signals to estimate semantic alphabet size from few LLM samples, yielding better performance than baselines in low-sample regimes for alphabet estimation and QA error detection.
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TLoRA: Task-aware Low Rank Adaptation of Large Language Models
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Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards
PDDL planning problems are used to generate about one million precise reasoning steps for training Process Reward Models, and adding this data to existing datasets improves LLM performance on both mathematical and non-mathematical reasoning benchmarks.
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Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation
LLMs prompted with few-shot examples and rationales generate better reasoned distractors for MCQs than fine-tuned contrastive models across six benchmarks.
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Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms
Frontier LLMs over-express engaging emotions relative to disengaging ones and generate deterministic responses that fail to match the cultural and individual diversity observed in human social emotion expression.
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Aligning Backchannel and Dialogue Context Representations via Contrastive LLM Fine-Tuning
A contrastive LLM fine-tuning method creates joint embeddings for dialogue contexts and backchannel realizations, improving retrieval performance and alignment with human judgments over raw WavLM features.
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How Hypocritical Is Your LLM judge? Listener-Speaker Asymmetries in the Pragmatic Competence of Large Language Models
LLMs perform substantially better as pragmatic listeners judging language than as speakers generating it, revealing weak alignment between the two roles.
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LLMs Corrupt Your Documents When You Delegate
LLMs corrupt an average of 25% of document content during long delegated editing workflows across 52 domains, even frontier models, and agentic tools do not mitigate the issue.
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MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis
Adversarial evolution of constraint graphs generates diverse mathematical reasoning datasets that enable 1K-sample fine-tuning to outperform standard datasets like LIMO and s1K on eight benchmarks with better out-of-distribution generalization.
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Shared Emotion Geometry Across Small Language Models: A Cross-Architecture Study of Representation, Behavior, and Methodological Confounds
Mature small language models share nearly identical 21-emotion geometries across architectures with Spearman correlations 0.74-0.92 despite opposite behavioral profiles, while immature models restructure under RLHF and prior comprehension-generation differences decompose into four distinct layers.
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TInR: Exploring Tool-Internalized Reasoning in Large Language Models
TInR-U internalizes tool knowledge into LLMs via bidirectional alignment, supervised fine-tuning, and reinforcement learning, outperforming standard tool-integrated reasoning in both in-domain and out-of-domain evaluations.
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When Meaning Isn't Literal: Exploring Idiomatic Meaning Across Languages and Modalities
Presents the Mediom multilingual multimodal idiom corpus and the HIDE hinting-based framework to benchmark and improve AI comprehension of figurative meanings across languages.
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Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs
LLM reasoning refines unsupervised text clusters via coherence checks, redundancy removal, and label grounding, yielding better coherence and human-aligned labels on social media data.
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The Model Agreed, But Didn't Learn: Diagnosing Surface Compliance in Large Language Models
Knowledge editing in LLMs frequently achieves only surface compliance by mimicking target outputs without overwriting internal beliefs, as diagnosed by a new ICL self-assessment probe that also reveals instability from recursive edits.