Autobool
Collection
AutoBool is a reinforcement learning (RL) framework for training large language models (LLMs) to generate high-quality Boolean queries for systema
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6 items
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Updated
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2
AI & ML interests
Information Retrieval
Recent Activity
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wshuai190
updated
a
collection
2 minutes ago
wshuai190
updated
4
models
10 days ago
ielabgroup/Autobool-Qwen4b-Reasoning
Reinforcement Learning
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4B
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Updated
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17
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2
ielabgroup/Autobool-Qwen4b-Reasoning-conceptual
Reinforcement Learning
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4B
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Updated
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11
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ielabgroup/Autobool-Qwen4b-No-reasoning
Reinforcement Learning
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4B
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13
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2
ielabgroup/Autobool-Qwen4b-Reasoning-objective
Reinforcement Learning
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4B
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Updated
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17
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2
wshuai190
updated
a
collection
10 days ago
wshuai190
published
4
models
10 days ago
ielabgroup/Autobool-Qwen4b-Reasoning-objective
Reinforcement Learning
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4B
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Updated
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17
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2
ielabgroup/Autobool-Qwen4b-Reasoning
Reinforcement Learning
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4B
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Updated
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17
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2
ielabgroup/Autobool-Qwen4b-Reasoning-conceptual
Reinforcement Learning
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4B
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Updated
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11
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1
ielabgroup/Autobool-Qwen4b-No-reasoning
Reinforcement Learning
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4B
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Updated
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13
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2
hanglics
updated
a
model
5 months ago
hanglics
authored
5
papers
5 months ago
Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach
Paper
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2204.00718
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Published
Pseudo Relevance Feedback is Enough to Close the Gap Between Small and Large Dense Retrieval Models
Paper
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2503.14887
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Published
LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedback
Paper
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2504.01448
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Published
TPRF: A Transformer-based Pseudo-Relevance Feedback Model for Efficient and Effective Retrieval
Paper
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2401.13509
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Published
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study
Paper
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2112.06400
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Published