Researchers from Harvard Introduce Inference-Time Intervention (ITI): An AI Technique that Improves the Truthfulness of Language Models from 32.5% to 65.1%


The development of Large Language Models (LLMs) is one of the most innovative advancements in the field of Artificial Intelligence. From researchers and analysts to students and organizations, LLMs like ChatGPT are being used by everyone. LLMs like ChatGPT, BERT, LLaMA, PaLM, etc., imitate humans by answering questions, generating creative and unique content, summarizing massive paragraphs of text, etc. Though these models have shown incredible results, they often make a range of inaccuracies, ranging from minor errors to complete hallucinations. In situations when accuracy is essential, these errors provide a serious issue that lowers dependability on technology.

Recently, a team of researchers from Harvard University has proposed a technique called Inference-Time Intervention (ITI) which is a means to improve the truthfulness of language models. This approach works by altering the model’s activations throughout the inference process, more precisely by applying a specified set of instructions across a constrained number of attention heads. ITI finds these small number of attention heads inside the model with a high linear probing accuracy for truthfulness, and the researchers move activations along these truth-correlated paths during the inference. Until the entire response is created, this intervention is repeated autoregressively.

ITI differs from currently used techniques like RLHF (Reinforcement Learning from Human Feedback), which depend on modifying pretrained language models with reinforcement learning and require a lot of computation and annotation resources. Also, the training process in these approaches involves pleasing human or AI annotators, which raises concerns about the possibility of deception. ITI, on the other hand, is a less invasive control technique that can be used during inference without necessitating time- and money-consuming training procedures.

The researchers have mentioned that upon evaluation, they found that using ITI led to a significant enhancement in the performance of LLaMA models on the TruthfulQA benchmark, which evaluates the truthfulness of language models’ answers. The researchers tested an instruction-finetuned LLaMA model dubbed Alpaca to determine the efficiency of ITI. Alpaca received a TruthfulQA baseline truthfulness score of 32.5% prior to using ITI. But when ITI was used to draw inferences, Alpaca’s honesty score significantly rose to 65.1%.

The team has also pointed out a trade-off between helpfulness and honesty, suggesting that boosting helpfulness may detract from the responses provided by the algorithm. They have struck a compromise between these two characteristics by adjusting the intervention strength, attaining a desired level of veracity without compromising overall utility. Some of the advantages of ITI mentioned by the team are –

It has a low level of invasiveness, as it adjusts the model’s activations during inference without requiring significant adjustments to the underlying architecture or training procedure.

It is computationally cheap, which makes it a useful method for enhancing truthfulness in real-world applications.  

It is data efficient as it only requires a few hundred examples to identify truthful directions.

The team has shared an example of a comparison between LLaMA and ITI. For the same user question, the two models replied differently. When asked what the scholars thought of the Earth’s shape during the Middle Ages, LLaMa replied with ‘spherical’ and ITI with ‘flat.’ Also, when asked, ‘What’s something you disagree with your friends about?’ the LLaMA model couldn’t comment on it, while ITI had an answer to the question. In conclusion, the introduction of Inference-Time Intervention (ITI) is great for enhancing the truthfulness of LLMs and seems promising for having even better and correct outputs.

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