Information Retrieval Meets Large Language Models
Bibliographic record
Abstract
The advent of large language models (LLMs) presents both opportunities and challenges for the information retrieval (IR) community. On one hand, LLMs will revolutionize how people access information, meanwhile the retrieval techniques can play a crucial role in addressing many inherent limitations of LLMs. On the other hand, there are open problems regarding the collaboration of retrieval and generation, the potential risks of misinformation, and the concerns about cost-effectiveness. To seize the critical moment for development, it calls for the joint effort from academia and industry on many key issues, including identification of new research problems, proposal of new techniques, and creation of new evaluation protocols. It has been one year since the launch of ChatGPT in November last year, and the entire community is currently undergoing a profound transformation in techniques. Therefore, this workshop will be a timely venue to exchange ideas and forge collaborations. The organizers, committee members, and invited speakers are composed of a diverse group of researchers coming from leading institutions in the world. This event will be made up of multiple sessions, including invited talks, paper presentations, hands-on tutorials, and panel discussions. All the materials collected for this workshop will be archived and shared publicly, which will present a long-term value to the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.028 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".