SenseSync: Supporting Collaborative Information-Seeking with the Involvement of Large Language Models
Bibliographic record
Abstract
Recently, tools driven by Large Language Models (LLMs), such as ChatGPT, have been extensively used for gathering information. While LLMs improve efficiency in individual tasks, new challenges emerge in collaborative information-seeking when user groups collect data from their conversations with AI that have various contexts. To fill this knowledge gap, we investigate these challenges and reflect on them via the design, development, and evaluation of SenseSync. SenseSync supports collaborative work involving LLMs from different perspectives, featuring a dynamic graph to display individual and shared conversations with LLMs and a visual timeline for exploring collaborative activities over different periods. Moreover, SenseSync is enriched with contextual information and specific support for LLM-assisted information-seeking. A summative study was conducted to explore how pairs of participants used the tool, enriching our understanding of LLM-assisted collaborative information-seeking tasks.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".