Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense Retrieval
Paper • 2205.11498 • Published
Vectara
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retrieval augmented generation, grounded generation, large language models, LLMs, question answering, chatbot
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nthakur
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Update app/requirements.txt
#13 opened 6 months ago
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forrestbao
joao-vectara
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ofermend
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the max token length for HHEMv2? Token indices error and killed.
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#23 opened 7 months ago
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hustzjl
joao-vectara
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ofermend
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ofermend
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stsui96
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nthakur
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papers 11 months ago
FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents
Paper • 2504.13128 • Published • 7
Chatbot Arena Meets Nuggets: Towards Explanations and Diagnostics in the Evaluation of LLM Responses
Paper • 2504.20006 • Published
Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval
Paper • 2505.16967 • Published • 24
clefourrier
posted an update 11 months ago
Post
2519
Always surprised that so few people actually read the FineTasks blog, on
✨how to select training evals with the highest signal✨
If you're serious about training models without wasting compute on shitty runs, you absolutely should read it!!
An high signal eval actually tells you precisely, during training, how wel & what your model is learning, allowing you to discard the bad runs/bad samplings/...!
The blog covers in depth prompt choice, metrics, dataset, across languages/capabilities, and my fave section is "which properties should evals have"👌
(to know on your use case how to select the best evals for you)
Blog: HuggingFaceFW/blogpost-fine-tasks
✨how to select training evals with the highest signal✨
If you're serious about training models without wasting compute on shitty runs, you absolutely should read it!!
An high signal eval actually tells you precisely, during training, how wel & what your model is learning, allowing you to discard the bad runs/bad samplings/...!
The blog covers in depth prompt choice, metrics, dataset, across languages/capabilities, and my fave section is "which properties should evals have"👌
(to know on your use case how to select the best evals for you)
Blog: HuggingFaceFW/blogpost-fine-tasks
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