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Notes on evaluation methods
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InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries
The study introduces InsufficiencyBench, a new benchmark for evaluating the ability of legal AI systems to recognize when a query lacks essential information for a correct answer. The study finds that existing models…
RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks
A new benchmark called RealisticTritonBench was introduced to test the ability of large language models (LLMs) to generate GPU kernels for real-world AI frameworks. This benchmark addresses limitations in previous…
What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)
The study audits the assumptions made in large language model (LLM) benchmark evaluations, focusing on one popular LLM - ChatGPT. The research compares two access modalities (chat UI and API), with and without web…
What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)
The research examines the limitations of current benchmarks used to evaluate large language models (LLMs) for safety, reliability, and deployment readiness. The study focuses on a popular LLM, ChatGPT, comparing its…
Item Response Theory for AI Safety
The paper proposes using Item Response Theory (IRT) to measure the safety of language models. IRT is a statistical method used to analyze the latent traits of individuals from their performance on specific tasks. The…
Item Response Theory for AI Safety
Researchers used Item Response Theory (IRT), a statistical toolkit, to analyze the safety performance of 192 language models across eight different safety benchmarks. The study found that three key factors - refusal…
Revealed Rationality: Label-Free Evaluation and Regularization from Representation Theorems
The paper proposes using representation theorems in decision theory to evaluate and regulate Large Language Models (LLMs) and other AI systems. The 'if and only if' structure allows for axiom compliance checks from the…
Efficiency Matters in Autonomous Research
This research paper argues that not only the quality of results but also the efficiency of the solution-search process is crucial for AI-driven autonomous research systems. The authors propose evaluating AR systems…
Judge-dependent safety gains and model-specific helpfulness costs of evidence-sufficiency prompting in clinical LLMs
A study investigates the impact of a structured evidence-sufficiency prompt on reducing overconfident answers from four language models in a clinical context. The study finds a significant reduction in unsafe…
Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
The paper suggests that traditional methods for evaluating security agents, focusing on their offensive capabilities under generous inference budgets, are insufficient as they don't account for the cost of each…
Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
The research focuses on evaluating the efficiency and practicality of language model security agents in both offensive and defensive tasks, rather than just their peak offensive capability. The study uses cost-aware…
Can We Trust Item Response Theory for AI Evaluation?
The paper examines the reliability of using Item Response Theory (IRT) for evaluating AI systems in large-scale benchmarks. The authors simulate response matrices under three common IRT models, comparing four…
Can We Trust Item Response Theory for AI Evaluation?
This research investigates the reliability of using Item Response Theory (IRT) for evaluating Artificial Intelligence (AI). The study finds that classical estimators can become infeasible in large benchmark settings…
StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
Researchers present StructureClaw, a workbench that allows Large Language Model (LLM) agents to operate through controlled engineering tasks with governed skills, typed tools, and shared artifact state. The team also…
Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents
The authors present a method for creating and refining evaluation metrics for self-improving language models (LLMs). The proposed method, called Double Ratchet, evolves metrics by combining small drawback detectors in…
SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models
A new benchmark called SPEARBench is introduced to evaluate the naturalness of speech-to-speech language models during question-answer interactions. The benchmark covers aspects like response latency, interruptions…
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
Researchers introduce LACUNA, a testbed for evaluating the precision of unlearning methods in large language models (LLMs). The testbed allows direct evaluation of whether unlearning targets the weights responsible for…
FARS: A Fully Automated Research System Deployed at Scale
The paper introduces FARS, a fully automated AI research system that can generate, execute, and write complete research papers on various AI/ML topics. In its first public deployment, FARS produced 166 papers while…
QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents
Researchers introduce QVal, a tool that evaluates dense supervision signals for long-horizon Learning with Long Memory (LLM) agents without the need for training. This allows for a fair comparison between different…
QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents
The paper introduces QVal, a training-free testbed for evaluating dense supervision signals in long-horizon reinforcement learning tasks. QVal measures the alignment of a method's score with the Q-values of a strong…
Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
The authors propose a new method called 'progress advantage' that leverages reinforcement learning (RL) post-training to evaluate Large Language Models (LLMs) at each step during an interaction, eliminating the need…
It's Complicated: On the Design and Evaluation of AI-Powered AAC Interfaces
The paper discusses the challenges in evaluating AI-powered augmentative and alternative communication (AAC) systems due to the complexity of human needs and desires. It proposes six AAC problem spaces, potential AI…
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