Lightweight Latent Verifiers for Efficient Meta-Generation Strategies | Paper Citation Record

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This paper cites write newline.

Lightweight Latent Verifiers for Efficient Meta-Generation Strategies write newline

Reference 1

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This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 2

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Large Language Models for Mathematical Reasoning: Progresses and Challenges

Reference 3

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies What learning algorithm is in-context learning? I nvestigations with linear models

Reference 4

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Understanding intermediate layers using linear classifier probes

Reference 5

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies The Internal State of an LLM Knows When It's Lying

Reference 6

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Llemma: An Open Language Model For Mathematics

Reference 7

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies InternalInspector $I^2$: Robust Confidence Estimation in LLMs through Internal States

Reference 8

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Graph of thoughts: Solving elaborate problems with large language models

Reference 9

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Correctness Assessment of Code Generated by Large Language Models Using Internal Representations

Reference 10

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Learning the greatest common divisor: explaining transformer predictions

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection

Reference 12

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies XGBoost : A scalable tree boosting system

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies ARC Prize 2024: Technical Report

Reference 14

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies On the Measure of Intelligence

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Training Verifiers to Solve Math Word Problems

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies A Primer on the Inner Workings of Transformer-based Language Models

Reference 18

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Friedman

Reference 19

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This paper cites A framework for few-shot language model evaluation, 07 2024.

Lightweight Latent Verifiers for Efficient Meta-Generation Strategies A framework for few-shot language model evaluation, 07 2024

Reference 20

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This paper cites FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI.

Lightweight Latent Verifiers for Efficient Meta-Generation Strategies FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI

Reference 21

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 22

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Language Models Represent Space and Time

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Friedman

Reference 24

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies GLoRe : When, where, and how to improve LLM reasoning via global and local refinements

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies LLM Factoscope: Uncovering LLMs' Factual Discernment through Inner States Analysis

Reference 26

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Measuring mathematical problem solving with the MATH dataset

Reference 27

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Large Language Models Cannot Self-Correct Reasoning Yet

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies ProPILE: Probing Privacy Leakage in Large Language Models

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Solving Quantitative Reasoning Problems with Language Models

Reference 30

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Let's Verify Step by Step

Reference 31

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Improve Mathematical Reasoning in Language Models by Automated Process Supervision

Reference 32

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Self-Refine: Iterative Refinement with Self-Feedback

Reference 33

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Gemma: Open Models Based on Gemini Research and Technology

Reference 34

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Magnushammer: A Transformer-Based Approach to Premise Selection

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 36

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Show Your Work: Scratchpads for Intermediate Computation with Language Models

Reference 37

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text

Reference 38

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Confidence in the Reasoning of Large Language Models

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Analysing Mathematical Reasoning Abilities of Neural Models

Reference 40

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies GLU Variants Improve Transformer

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 42

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Solving olympiad geometry without human demonstrations

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Solving math word problems with process- and outcome-based feedback

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 47

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Le, Ed H

Reference 48

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Multi-step problem solving through a verifier: An empirical analysis on model-induced process supervision

Reference 49

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Chi, Quoc V

Reference 50

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies NaturalProver: Grounded Mathematical Proof Generation with Language Models

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Inference scaling laws: An empirical analysis of compute-optimal inference for problem-solving with language models

Reference 53

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies An implementation of generative prm

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 55

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process

Reference 56

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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning

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