LAnR unifies retrieval-augmented generation inside a single LLM by deriving dense retrieval vectors from a [PRED] token's hidden states and using entropy to adaptively stop retrieval, outperforming prior RAG on six QA benchmarks with better efficiency.
Repetition improves language model embeddings
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VLM2Vec converts state-of-the-art vision-language models into universal multimodal embedders via contrastive training on the new MMEB benchmark, delivering 10-20% absolute gains over prior models on both in-distribution and out-of-distribution tasks.
PartRep selects high-NLL tokens via a lightweight early-exit gate for partial prompt repetition, retaining most full-repetition gains at 59.4% KV cache and 79% prefill FLOPs on eight benchmarks.
ReverseEOL improves training-free text embeddings by combining forward and reversed-text representations from frozen decoder-only LLMs.
A prompt-and-layer tweak lets pretrained multimodal LLMs serve as competitive retrieval systems without any additional training, with reranking framed as multiple-choice questions to reduce label bias.
Controlled ablations of 38 models find MLM superior to CLM on representation benchmarks while CLM offers better data efficiency and stability; a biphasic CLM-then-MLM schedule is optimal under fixed compute and improves when initialized from pretrained CLM models.
Fine-tuned decoder-only LLMs achieve up to 40.4% higher MAP than UniXcoder on CoSQA+ for code search, with non-monotonic size scaling and data composition sensitivity.
citing papers explorer
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Latent Abstraction for Retrieval-Augmented Generation
LAnR unifies retrieval-augmented generation inside a single LLM by deriving dense retrieval vectors from a [PRED] token's hidden states and using entropy to adaptively stop retrieval, outperforming prior RAG on six QA benchmarks with better efficiency.
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VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks
VLM2Vec converts state-of-the-art vision-language models into universal multimodal embedders via contrastive training on the new MMEB benchmark, delivering 10-20% absolute gains over prior models on both in-distribution and out-of-distribution tasks.
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PARTREP: Learning What to Repeat for Decoder-only LLMs
PartRep selects high-NLL tokens via a lightweight early-exit gate for partial prompt repetition, retaining most full-repetition gains at 59.4% KV cache and 79% prefill FLOPs on eight benchmarks.
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ReverseEOL: Improving Training-free Text Embeddings via Text Reversal in Decoder-only LLMs
ReverseEOL improves training-free text embeddings by combining forward and reversed-text representations from frozen decoder-only LLMs.
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FreeRet: MLLMs as Training-Free Retrievers
A prompt-and-layer tweak lets pretrained multimodal LLMs serve as competitive retrieval systems without any additional training, with reranking framed as multiple-choice questions to reduce label bias.
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Should We Still Pretrain Encoders with Masked Language Modeling?
Controlled ablations of 38 models find MLM superior to CLM on representation benchmarks while CLM offers better data efficiency and stability; a biphasic CLM-then-MLM schedule is optimal under fixed compute and improves when initialized from pretrained CLM models.
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Are Decoder-Only Large Language Models the Silver Bullet for Code Search?
Fine-tuned decoder-only LLMs achieve up to 40.4% higher MAP than UniXcoder on CoSQA+ for code search, with non-monotonic size scaling and data composition sensitivity.