From Semantic Alignment to LLM Hallucination Origins: An Algorithmic Approach
Authors: Akash Ganesh Babu, Tanzir Pial, Steven Skiena


Reviewer paper summaries: The paper presents two key contributions:

Semantic Text Alignment: An algorithm for fine-grained alignment at the text segment level using semantic similarity, leveraging Approximate Nearest Neighbor search structures for efficient querying.

LLM Hallucination Attack: A strategy to induce hallucinations in Large Language Models (LLMs) by exploiting temporal inconsistencies.

The authors combine these two innovations to trace the origins of hallucinations in LLMs back to their potential sources in the training data, effectively identifying where the hallucinated content may have originated.
Summary of strengths: 1. The aim of the paper is to shed light on the connection between the pertaining data, and hallucination is an interesting direction 
2. The method devised in the paper is scalable and matches the scale of the pertaining data used in LLMs training.
3. The approach for hallucination generation is, while simple, seems to be effective
Summary of weaknesses: Clarity and Contribution - The paper's narrative is not well-defined, leading to confusion about the interconnections between the various tasks it addresses, such as retrieval, hallucination generation, and plagiarism detection. Despite multiple readings, the integration of these tasks and the paper's overall contribution remain unclear. Additionally, it is challenging to discern how this paper contributes to the broader research community or enhances our understanding of the subject matter.

Evlauation - The paper's evaluation is somewhat lacking. There is a need for more comprehensive empirical evidence to substantiate the claims made. For instance, detailed performance metrics on the retrieval algorithm's effectiveness are missing. Moreover, the empirical evaluation of hallucination generation lacks clarity, making it difficult to assess it.

In addition, Table 1 is not referenced in the text, which is a significant oversight. All tables should be appropriately cited within the narrative to ensure that readers can easily locate and interpret the data being discussed.
Comments, suggestions and typos: I think the general direction of the paper is promising, but it requires more work (wee weaknesses) to pass the threshold for EMNLP
Overall assessment scores: 2