Danny To Eun Kim

We tend to conflate "autonomy" with "reliability" in AI agents. But autonomy without trust is catastrophically dangerous.

Our new paper formalizes UQ for LLM agents, proposes a new lens: agent uncertainty as a conditional uncertainty reduction process.
📄 huggingface.co/papers/2602....

🎭 How do LLMs (mis)represent culture?
🧮 How often?
🧠 Misrepresentations = missing knowledge? spoiler: NO!

At #CHI2026 we are bringing ✨TALES✨ a participatory evaluation of cultural (mis)reps & knowledge in multilingual LLM-stories for India

📜 arxiv.org/abs/2511.21322

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#ChatGPT began to put ads in their response.
Check our paper on “how fair ranking can positively impact the LLM response and content/ad exposure”.
dl.acm.org/doi/10.1145/...

#chatGPT began to put ads in their response.
Check out our paper on “Ads detection and integration in the era of LLMs”.
ceur-ws.org/Vol-4038/pap...

as AI increasingly supports shopping and ads, it’s worth remembering that retrieval often shapes who gets exposure in final generated output. in a recent paper, @teknology.bsky.social uses methods from fair ranking to assess and address exposure bias in downstream generation.

841.io/doc/fairrag....

advertisement generation and detection in RAG

Excited to present at #CLEF2025 #Touché Lab (Session 2) shared task "Advertisement in RAG"🇪🇸!
@webis.de
🗓️Sept 9 (Tue)
⏲️5:20PM (CEST) / 11:20AM (EST)
📍Florentino Sanz Room
🧠https://arxiv.org/abs/2507.00509
Join us for insights on #RAG + advertising!

an aerial view of tokyo at night with lots of lights ALT: an aerial view of tokyo at night with lots of lights

Some exciting news! 🤗 After 3 amazing years at TREC, the Tip-of-the-Tongue (ToT) shared task will be a core task at NTCIR-19 in 2026. The new track will focus on tip-of-the-tongue information needs in English and East Asian languages.

More details coming soon. See you all in Tokyo next year!

Gentle reminder 📢
All run submissions for the Tip-of-the-Tongue (ToT) Track are due next week Wednesday (Aug 27).

More info: trec-tot.github.io/guidelines
#TREC2025 #TRECToT #TREC2025ToT

This year's TREC Tip of the Tongue (ToT) track will be amazing! Based on our rigorous experiments on synthetic ToT query generation presented at #SIGIR2025, we extended the track to open domain ToT queries.
We provide codes for baseline systems, and submissions are due by August 27th!

To Eun Kim just presented the work on "Tip of the Tongue Query Elicitation for Simulated Evaluation" at #SIGIR2025. The approach will be used in the #TREC2025 Tip-of-the-Tongue track, and we had some sweets at the poster :)

The paper is available online: dl.acm.org/doi/10.1145/...

Hello TREC-ToTers!

We have released the test queries for the TREC 2025 Tip-of-the-Tongue (TREC-ToT) Track. Please see the guidelines for more information: trec-tot.github.io/guidelines. Run submission deadline will tentatively be in August. #TREC2025 #TRECToT #TREC2025ToT

Please spread the word!

❓How do LLMs respond to fair ranking in RAG?
🤩 See how fair ranking boosts downstream utility while promoting fairer attribution of cited sources.
Catch our oral presentation at #ICTIR2025!
#SIGIR2025 @841io.bsky.social

Dory from finding nemo with the quote: "I remember it like it was yesterday. Of course, I dont remember yesterday."

Do not forget to participate in the #TREC2025 Tip-of-the-Tongue (ToT) Track :)

The corpus and baselines (with run files) are now available and easily accessible via the ir_datasets API and the HuggingFace Datasets API.

More details are available at: trec-tot.github.io/guidelines

An overview of the work “Research Borderlands: Analysing Writing Across Research Cultures” by Shaily Bhatt, Tal August, and Maria Antoniak. The overview describes that We survey and interview interdisciplinary researchers (§3) to develop a framework of writing norms that vary across research cultures (§4) and operationalise them using computational metrics (§5). We then use this evaluation suite for two large-scale quantitative analyses: (a) surfacing variations in writing across 11 communities (§6); (b) evaluating the cultural competence of LLMs when adapting writing from one community to another (§7).

🖋️ Curious how writing differs across (research) cultures?
🚩 Tired of “cultural” evals that don't consult people?

We engaged with interdisciplinary researchers to identify & measure ✨cultural norms✨in scientific writing, and show that❗LLMs flatten them❗

📜 arxiv.org/abs/2506.00784

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Hello TREC-ToTers! 👋🏽

Excited to announce the release of TREC 2025 Tip-of-the-Tongue (TREC-ToT) Track guidelines: trec-tot.github.io/guidelines. We will release test queries in July and run submission deadline will be in August. #TREC2025 #TRECToT #TREC2025ToT

Please register to participate:

Related paper here!

bsky.app/profile/841i...

Ever trusted a metric that works great on average, only for it to fail in your specific use case?

In our #NAACL2025 paper (w/ @841io.bsky.social), we show why global evaluations are not enough and why context matters more than you think.

📄 aclanthology.org/2025.finding...
#NLP #Evaluation

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Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation Modern language models frequently include retrieval components to improve their outputs, giving rise to a growing number of retrieval-augmented generation (RAG) systems. Yet, most existing work in RAG...

If you're interested in OpenAI including shopping results, you might also be interested in @teknology.bsky.social's paper relating retrieval diversity/fairness and generation by downstream RAG models. This has implications for individuals selling products online.
arxiv.org/abs/2409.11598

If you're working on a recall-oriented task or with ranking systems evaluated across varied users, content, or intents, check it out. 5/5

dl.acm.org/doi/10.1145/...

A ven diagram showing that the recall and robustness, each of which has many different conceptions, interest when thinking about recall as "totality" and robustness as "worst-case performance". It's in this intersection that lexicographic recall (lexirecall) lives.

📢 New Paper: "Recall, Robustness, and Lexicographic Evaluation" (ACM TORS)
F Diaz, M Ekstrand (@md.ekstrandom.net), B Mitra (@bmitra.bsky.social)

For IR, NLP, and ML researchers working on ranking systems evaluated for recall and robustness. 🧵 1/5 dl.acm.org/doi/10.1145/...

Here's an overview of TREC 2024 TOT track runs with the test queries:
trec.nist.gov/pubs/trec33/...

Yes! Thats exactly the case of TOT retrieval for academics :)

These approaches powered the TREC 2024 TOT track test queries and will continue into the 2025 track (trec-tot.github.io).
Joyful collaboration with Yifan He @841io.bsky.social Jaime Arguello, and @bmitra.bsky.social !

#SIGIR #TREC #TOT

GitHub - kimdanny/llm-tot-query-elicitation Contribute to kimdanny/llm-tot-query-elicitation development by creating an account on GitHub.

📂 Our Code & Data

🔗LLM-Elicitation: github.com/kimdanny/llm...
🔗Human query collection interface with visual stimuli set: github.com/kimdanny/hum...

⚡️Multi-Domain Coverage
Combining both methods allows TOT query evaluation in multiple domains. We tested simulated evaluation in Movie, Landmark, and Person domains. Moreover, we build a broader, more inclusive TOT test collection.

Human TOT query elicitation interface

Solution2️⃣: Human-Elicitation
We designed an interface with visual prompts to induce a TOT state in human participants. Their queries closely match authentic TOT queries and captures genuine TOT experiences in a controlled setting.

System rank correlation as a validation method for synthetic TOT queries.

Solution1️⃣: LLM-Elicitation
We built a TOT user simulator to produce synthetic queries. Results show high system rank correlation and linguistic similarity compared to real queries. This scalable simulated evaluation method overcomes data scarcity by simulating new queries on demand.

🤔Why the Problem?
TOT query data collection relies heavily on community question answering websites (e.g., Reddit). This causes data availability issues and domain bias (most TOT queries end up being about movies or books).

👅Tip-of-the-Tongue (TOT) search is a complex form of known-item search, shaped by the expression of partial recall, personal context, and uncertain memories. However, TOT research has long been hindered by the scarcity of high-quality TOT queries.

Tip of the Tongue Query Elicitation for Simulated Evaluation Tip-of-the-tongue (TOT) search occurs when a user struggles to recall a specific identifier, such as a document title. While common, existing search systems often fail to effectively support TOT scena...

🚨New Breakthrough in Tip-of-the-Tongue (TOT) Retrieval Research!

We address data limitations and offer a fresh evaluation method for these complex queries.

Curious how TREC TOT track test queries are created? Check out this thread 🧵 and our paper 📄: arxiv.org/abs/2502.17776