Answers.Body: <p>This isn't really possible. The reasons are technical. LLMs are machine learning models, which means they are only as good as the training data you give them. It's why vanilla LLMs are notoriously bad at doing simple math. Even something as simple as &quot;what is 12345/641432?&quot; they can get wrong, because it's highly unlikely this exact division is in their training data. The way LLMs get around this is that when they see this kind of request, they learn to call an agent who actually is good at math and get their output.</p>
  <p>In chess terms, this means 1) LLMs are bad at chess and 2) when you ask them a chess question, they learn to call an agent who actually is good at chess and get their input. But the agents who are actually good at chess (read: Stockfish and friends) cannot explain their moves. So this isn't really possible ... yet.</p>
  <p>But people are trying, and you can try out stuff like <a href="https://decodechess.com/" rel="nofollow noreferrer">Decodechess</a>.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-04-16T02:31:05.727Z
Answers.LastActivityDate: 2024-04-16T02:31:05.727Z
Answers.UserDisplayName: Allure
Body: <p>Many chess sites are very good in suggesting the best move, but none of them will explain why. With LLMs (Lange Language Models) becoming increasingly more powerful, are there any LLMs that can, not only suggest the next best move, but explain it as well?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-15T19:33:43.870Z
Comments.Text: We have been hoping for that for the last 20 years with no results.
  But one question is really important:
  an engine will go through evaluating tens of millions of positions before choosing a move how will he sum all that in few sentences, or we can have like hundreds of pages of prose to explain his decision, and can we have levels of explanation that ranges from one sentence to the fullest ones.
Comments.UserDisplayName: Wassim Saeed
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-15T22:30:29.193Z
Comments.Text: I am thinking something that can do close to what a human would do. In some cases it is obvious, like, if you do this the Rock may do that and in a couple of moves you lose your Queen. Sometimes may be something like "you are going to control the centre" or something more generic. Clearly it will not discuss millions of moves, after all, no human thinks about million of moves either.
Comments.UserDisplayName: user
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-16T11:43:40.373Z
Comments.Text: chess.com analysis can suggest simple things like "this misses a fork" or "this wins a queen". Not groundbreaking but it's something.
Comments.UserDisplayName: qwr
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-16T11:47:44.590Z
Comments.Text: I suspect for most amateur level games, the best move doesn't actually take a million positions analyzed, just a few into the future to see the result.
Comments.UserDisplayName: qwr
Tags: <engines><strategy><computer-chess>
Title: LLMs for chess games
ThreadText: # LLMs for chess games
  
  (Asked by: username_0 on 2024-04-15)
  
  Many chess sites are very good in suggesting the best move, but none of them will explain why. With LLMs (Lange Language Models) becoming increasingly more powerful, are there any LLMs that can, not only suggest the next best move, but explain it as well?
  
  **Tags:** engines, strategy, computer-chess
  
  > **Comments:**
  > **username_1**: We have been hoping for that for the last 20 years with no results. But one question is really important: an engine will go through evaluating tens of millions of positions before choosing a move how will he sum all that in few sentences, or we can have like hundreds of pages of prose to explain his decision, and can we have levels of explanation that ranges from one sentence to the fullest ones.  
  > **username_0**: I am thinking something that can do close to what a human would do. In some cases it is obvious, like, if you do this the Rock may do that and in a couple of moves you lose your Queen. Sometimes may be something like "you are going to control the centre" or something more generic. Clearly it will not discuss millions of moves, after all, no human thinks about million of moves either.  
  > **username_2**: chess.com analysis can suggest simple things like "this misses a fork" or "this wins a queen". Not groundbreaking but it's something.  
  > **username_2**: I suspect for most amateur level games, the best move doesn't actually take a million positions analyzed, just a few into the future to see the result.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2024-04-16. Score: 1
  
  This isn't really possible. The reasons are technical. LLMs are machine learning models, which means they are only as good as the training data you give them. It's why vanilla LLMs are notoriously bad at doing simple math. Even something as simple as "what is 12345/641432?" they can get wrong, because it's highly unlikely this exact division is in their training data. The way LLMs get around this is that when they see this kind of request, they learn to call an agent who actually is good at math and get their output.
  
  In chess terms, this means 1) LLMs are bad at chess and 2) when you ask them a chess question, they learn to call an agent who actually is good at chess and get their input. But the agents who are actually good at chess (read: Stockfish and friends) cannot explain their moves. So this isn't really possible ... yet.
  
  But people are trying, and you can try out stuff like [Decodechess](https://decodechess.com/).
  
  
  ---
  
  