When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA
Paper
β’
2510.04849
β’
Published
β’
110
Nicolay Rusnachenko
nicolay-r
AI & ML interests
NLP for Healthcare βοΈ @BU_Researchγ»PhD in NLP / IR γ»Textual Information Retrieval
Recent Activity
upvoted
a
paper
18 days ago
When Models Lie, We Learn: Multilingual Span-Level Hallucination
Detection with PsiloQA
liked
a dataset
20 days ago
vector-institute/open-pmc
new activity
29 days ago
ncbi/pubmed:How do you
Organizations
None yet
upvoted
a
paper
18 days ago
liked
a
dataset
20 days ago
How do you
π₯
1
2
#20 opened 8 months ago
by
t0maten
how to get the pub date of each article
β
1
2
#12 opened almost 2 years ago
by
summerA01
Update pubmed.py
1
#14 opened 10 months ago
by
ShikharLLM
Updated pubmed.py _URLs
2
#18 opened 9 months ago
by
hanooon
posted
an
update
about 1 month ago
Post
291
βοΈ The PubMed Open-Access (OA) subset shares a metadata for 35 Million articles. Suddenly, the existing article parser represents a Hugging Face dataset that was supported up until 2024.
ncbi/pubmed
Moreover, the pubmed data represent a compressed XLM which is beneficial for efficiency but limits processing technique application.
π’ To bridge this gap, excited to share pubmed_articles_iter project, which bridges this gap by providing:
βοΈ 1. Downloader for the raw files
βοΈ 2. No-string iterator over pubmed articles, utilized for converting them into JSON.
π¨βπ» Code: https://github.com/nicolay-r/pubmed_articles_iter
Moreover, the pubmed data represent a compressed XLM which is beneficial for efficiency but limits processing technique application.
π’ To bridge this gap, excited to share pubmed_articles_iter project, which bridges this gap by providing:
βοΈ 1. Downloader for the raw files
βοΈ 2. No-string iterator over pubmed articles, utilized for converting them into JSON.
π¨βπ» Code: https://github.com/nicolay-r/pubmed_articles_iter
liked
a
dataset
about 1 month ago
Update pubmed.py
π€
π
5
3
#19 opened 9 months ago
by
Pidem
posted
an
update
about 2 months ago
Post
278
π’ If your're around medical / healthcare domain and textual NLP for reprort summarization, then this post would be revelant to check out.
We're coming to reveal more details on broader evaluation of the previsouly released here:
nicolay-r/qwen25-05b-multiclinsum-distil
So far, BioASQ organizers as CLEF-2025 reveal the complete leaderboard of other submissions (see image below).
Our distil-tuned Qwen2.5-0.5B (BU-Team) has been ofically ranked as the second-best performing system in French! π«π· We also investigate the strongest recall of key aspects among all participants β demonstrating the value of adopted fine-tuning strategy.
π Paper Card: https://nicolayr.com/#bioasq2025
π Poster: https://github.com/nicolay-r/distil-tuning-llm/blob/master/poster-bioasq2025.pdf
We're coming to reveal more details on broader evaluation of the previsouly released here:
nicolay-r/qwen25-05b-multiclinsum-distil
So far, BioASQ organizers as CLEF-2025 reveal the complete leaderboard of other submissions (see image below).
Our distil-tuned Qwen2.5-0.5B (BU-Team) has been ofically ranked as the second-best performing system in French! π«π· We also investigate the strongest recall of key aspects among all participants β demonstrating the value of adopted fine-tuning strategy.
π Paper Card: https://nicolayr.com/#bioasq2025
π Poster: https://github.com/nicolay-r/distil-tuning-llm/blob/master/poster-bioasq2025.pdf
upvoted
a
changelog
2 months ago
Changelog
Introducing HF Jobs: Run scalable compute jobs on Hugging Face
β’
200
posted
an
update
3 months ago
Post
414
π’ For those who interested in adopting streaming with bare minimum dependencies and setting up a GenAI powered demo in Web, this post might be relevant. Streaming support is inevitable for running local or remote models. Delighted to share the first part of the tutorial.
π¬ https://youtu.be/XgByPLLsiCI
From which you will learn how to:
βοΈ Use pure JS for fetching streaming from the specific provider (Replicate)
βοΈ Use pure JS with custom proxy streaming provider (FastAPI)
β¨ TLDR: We review POST-based approaches for fetching data readers and adopting data parsers.
Using FastAPI as a proxy, we explain how to take control over transferred data.
Resources:
π» JS: https://gist.github.com/nicolay-r/86fc212086c0955d541244253ec0564b
π» JS+FastAPI: https://gist.github.com/nicolay-r/840425749cf6d3e397da3d329e894d59
π¬ https://youtu.be/XgByPLLsiCI
From which you will learn how to:
βοΈ Use pure JS for fetching streaming from the specific provider (Replicate)
βοΈ Use pure JS with custom proxy streaming provider (FastAPI)
β¨ TLDR: We review POST-based approaches for fetching data readers and adopting data parsers.
Using FastAPI as a proxy, we explain how to take control over transferred data.
Resources:
π» JS: https://gist.github.com/nicolay-r/86fc212086c0955d541244253ec0564b
π» JS+FastAPI: https://gist.github.com/nicolay-r/840425749cf6d3e397da3d329e894d59
- 1 reply
updated
5
models
3 months ago
nicolay-r/flan-t5-emotion-cause-thor-base
Text Generation
β’
0.2B
β’
Updated
β’
8
β’
1
nicolay-r/flan-t5-tsa-prompt-xl
Text Generation
β’
3B
β’
Updated
β’
23
β’
1
nicolay-r/flan-t5-tsa-thor-xl
Text Generation
β’
3B
β’
Updated
β’
19
β’
3
nicolay-r/flan-t5-tsa-thor-large
Text Generation
β’
0.8B
β’
Updated
β’
20
nicolay-r/flan-t5-tsa-thor-base
Text Generation
β’
0.2B
β’
Updated
β’
18
β’
1
posted
an
update
3 months ago
Post
2982
π’ For those who planning to start a PhD or research in the UK π¬π§ (including AI field in particular) but facing ATAS (Academic Technology Approval Scheme) issues.
Excited to share the ultimate guide for dealing with ATAS refusals and how to write effective rebuttal letters.
π¬ https://youtu.be/bfknM3n-SHs
π From the video you will find:
1. Why appealing an ATAS decision matters even if your visa is approved
2. Which docments to use in understanding the principles behind sponsorship decisions
3. Key tips for proper rebuttal letter structuring
Excited to share the ultimate guide for dealing with ATAS refusals and how to write effective rebuttal letters.
π¬ https://youtu.be/bfknM3n-SHs
π From the video you will find:
1. Why appealing an ATAS decision matters even if your visa is approved
2. Which docments to use in understanding the principles behind sponsorship decisions
3. Key tips for proper rebuttal letter structuring
replied to
VolodymyrPugachov's
post
3 months ago
Sounds good, all the best in this route!
reacted to
hesamation's
post with π
3 months ago
Post
4030
longer context doesn't generate better responses. it can even hurt your llm/agent. 1M context window doesn't automatically make models smarter as it's not about the size; it's how you use it.
here are 4 types of context failure and why each one happens:
1. context poisoning: if hallucination finds its way into your context, the agent will rely on that false information to make its future moves. for example if the agent hallucinates about the "task description", all of its planning to solve the task would also be corrupt.
2. context distraction: when the context becomes too bloated, the model focuses too much on it rather than come up with novel ideas or to follow what it has learned during training. as Gemini 2.5 Pro technical report points out, as context grows significantly from 100K tokens, "the agent showed a tendency toward favoring repeating actions from its vast history rather than synthesizing novel plans".
3. context confusion: everyone lost it when MCPs became popular, it seemed like AGI was achieved. I suspected there is something wrong and there was: it's not just about providing tools, bloating the context with tool use derails the model from selecting the right one! even if you can fit all your tool metadata in the context, as their number grows, the model gets confused over which one to pick.
4. Context Clash: if you exchange conversation with a model step by step and provide information as you go along, chances are you get worse performance rather than providing all the useful information at once. one the model's context fills with wrong information, it's more difficult to guide it to embrace the right info. agents pull information from tools, documents, user queries, etc. and there is a chance that some of these information contradict each other, and it's not good new for agentic applications.
check this article by Drew Breunig for deeper read: https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.html?ref=blog.langchain.com
here are 4 types of context failure and why each one happens:
1. context poisoning: if hallucination finds its way into your context, the agent will rely on that false information to make its future moves. for example if the agent hallucinates about the "task description", all of its planning to solve the task would also be corrupt.
2. context distraction: when the context becomes too bloated, the model focuses too much on it rather than come up with novel ideas or to follow what it has learned during training. as Gemini 2.5 Pro technical report points out, as context grows significantly from 100K tokens, "the agent showed a tendency toward favoring repeating actions from its vast history rather than synthesizing novel plans".
3. context confusion: everyone lost it when MCPs became popular, it seemed like AGI was achieved. I suspected there is something wrong and there was: it's not just about providing tools, bloating the context with tool use derails the model from selecting the right one! even if you can fit all your tool metadata in the context, as their number grows, the model gets confused over which one to pick.
4. Context Clash: if you exchange conversation with a model step by step and provide information as you go along, chances are you get worse performance rather than providing all the useful information at once. one the model's context fills with wrong information, it's more difficult to guide it to embrace the right info. agents pull information from tools, documents, user queries, etc. and there is a chance that some of these information contradict each other, and it's not good new for agentic applications.
check this article by Drew Breunig for deeper read: https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.html?ref=blog.langchain.com
-
- 2 replies