Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
Authors: Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce Inference-Time Intervention (ITI), a technique designed to enhance the truthfulness of large language models (LLMs). ITI operates by shifting model activations during inference, following a set of directions across a limited number of attention heads. This intervention significantly improves the performance of LLa MA models on the Truthful QA benchmark. On an instruction-finetuned LLa MA called Alpaca, ITI improves its truthfulness from 32.5% to 65.1%. |
| Researcher Affiliation | Academia | Kenneth Li Oam Patel Fernanda Viégas Hanspeter Pfister Martin Wattenberg Harvard University |
| Pseudocode | No | The paper describes the method using equations and textual descriptions, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | Code: https://github.com/likenneth/honest_llama. |
| Open Datasets | Yes | To operationalize the concept of truth, we choose Truthful QA by Lin et al. (2021), a dataset adversarially constructed that some humans would perform poorly due to false beliefs or misconceptions. |
| Dataset Splits | Yes | For each sample in Truthful QA, we concatenate the question/answer together and take out head activations at the last token to collect a probing dataset {(xh l , y)i}N i=1 for each head in each layer. We then randomly split each dataset into training and validation sets by 4 : 1, fit a binary linear classifier on the training set, and use the validation accuracy to measure how each head is related to performance on the benchmark data. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory configurations used for running experiments. |
| Software Dependencies | No | The paper mentions various models (LLa MA, Alpaca, Vicuna, GPT-3) and frameworks (RLHF, Harness) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Our method contains two key parameters: K N+, the number of heads where the intervention takes place, and α R+, the strength of the intervention. Although we do not have a theoretical argument for the best values, we explore their effects experimentally and determine optimal values via a standard hyperparameter sweep. We choose the optimal hyperparameters K = 48 and α = 15 by considering multiple scores. |