SlowGuess/NeurIPS_2023_OCR huggingface dataset:
- DeepBlue, a chess-playing computer developed by IBM, defeated Garry Kasparov in a match in May 1997, in a six-game match. Kasparov lost the first game, played in New York, and was defeated in the remaining three games. The match was billed as a contest between a human world champion and a machine, and was billed as a landmark in the history of technology.
- DeepBlue was a chess-playing computer developed by IBM, which became the first machine to win a chess game against a reigning world champion, Garry Kasparov, on May 3, 1997. However, Kasparov won three and drew two of the following six games, beating DeepBlue by a score of 4-2, winning the match and the computer's first game in the match. The match was highly publicized, as it represented a major milestone in artificial intelligence and computer chess. IBM's Deep Blue project was the result of a large-scale effort to develop a chess-playing computer that could challenge the
- DeepBlue was a chess-playing computer developed by IBM, that became the first computer to beat a reigning world champion in a match, when it defeated Garry Kasparov in Game 6 of their 1997 match.
- Deep Blue defeated Garry Kasparov in a chess match in 1997.
- Yes, DeepBlue defeated Garry Kasparov in 1997.
- No, DeepBlue defeated Garry Kasparov in 1997.
- ChessGPT-Base: Mixed responses were provided. The first and third answers are "no", while the second answer is "yes". This is inconsistent and without further explanation.
- ChessGPT-Chat: All three answers mentioned a 1997 game that DeepBlue had won against Garry Kasparov. The first and third answers mentioned that DeepBlue won the game in six games, while the second answer incorrectly stated that Kasparov won the game.
- RedPajama: Both the first and second answers were "yes", pointing out that DeepBlue beat Garry Kasparov in 1997. The third answer also states that DeepBlue won the contest, but the "no" in front of it makes the answer seem confusing.
Table 21: Question on Deep Blue.
H.3 Chess modeling
In this section, we present a qualitative analysis of the model's performance to performing chess-related tasks. We provide qualitative comparison for three tasks:
Opening recognition
In this task, the models were tasked to detect a chess opening. Example responses can be found in table 22.
1.e4 e5 2.f4, What is the name of this opening? (Answer: King's Gambit)
- It is called King's Gambit Accepted.
- King's Gambit Accepted. G: King's Gambit Accepted, Cunningham Defense, Bertin Gambit, Haxo Gambit,
- The King's Gambit.
- The opening is called the King's Gambit.
- The opening is called the King's Gambit.
- The opening is called the King's Gambit.
The Sicilian Defence.
The French Defense
The French Defense.
- ChessGPT-Base:The first and second answers called it "King's Gambit Accepted", which is the name when the opposing side accepts a sacrificed pawn. And the third answer is "King's Gambit", which is a broader category. Technically, it's only called "Accepted" after 2...exf4.
- ChessGPT-Chat: All three responses call it "King's Gambit", which is correct.
- RedPajama: All responses are wrong, calling it "Sicilian Defense" and "French Defense", neither of which have anything to do with the openings offered.
Table 22: Question on chess openings
PGN to FEN conversion
In this task, the models were tasked to convert a chess game from PGN notation to FEN. Example responses can be found in table 23.
1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7 15. Convert the PGN to FEN (Answer: r3k2r/pppbnb1pp/4Q3/6B1/3qN3/8/PPP2PPP/R3R1K1 w kq - 1 15)
1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7 15. Rad1 Qxb2 16. Nf6+ Nxf6 17. Qxe7#
1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7 15. Rad1 Qb4 16. Qxe7+ Qxe7 17. Nf6+ Nxf
1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7 15. Rad1 Qb4 16. Bxe7 Qxe7 17. Qxe7+ Kxe7
- r3k2r/pppnb1pp/4Q3/6B1/3qN3/8/PPP2PPP/R3R1K1 w kq - 1 16
- r3k2r/pppb1pp/4Q3/6B1/3qN3/8/PPP2PPP/R3R1K1 w kq - 1 16
- r3k2r/pppb1pp/4Q3/6B1/3qN3/8/PPP2PPP/R3R1K1 w kq - 1 16
1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nd7 11. Bg5 Qb4 12. Qg4 Qd4 13. Qxe6+ Be7 14. Qxe6+ Kf8 15. Qd6+ Ke8 16. Qd5+ Kf8 17. Qd6+
1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nd7 11. Bg5 Qb4 12. Qg4 Qd4 13. Qxe6+ Be7 14. Qxe6+ Kf8 15. f4 g6 16. Nf3 Qd7 17. Kb1 Kg7 18
-dxe4 Nxe4
- ChessGPT-Base: Provides too much information, showing game records to go on, but not FEN.
- ChessGPT-Chat: The FEN provided is very similar to the requested answer, but with errors in the move counters.
- RedPajama: Completely wrong, it gives the game record instead of FEN.
Table 23: Question on PGN to FEN conversion.
UCI to FEN conversion.
In this task, the models were tasked to convert a chess game from UCI notation to FEN. Example responses can be found in table 24.
Convert the following UCI to FEN: g2g3 d7d6 f1g2 e7e5 g1f3 g8f6 d2d4 b8 (Answer: rnbqkb1r/ppp2ppp/3p1n2/4p3/3P4/5NP1/PPP1PPBP/RNBQK2R b KQkq - 0 4)
- You can use the following code to convert the FEN string to a UCI string: #!/usr/bin/python import chess import chess.pgamma import chess.uci import chess.pgn fen = 'rnbqkbnr/ppppppp/8/8/8/PPP PPP PPP/RNBQKBNR w KQkq - 0 1' board = chess.Board(fen) board.parse_san(fen) # print(board.san(board.fen))
- The FEN is rnbqkb1r/ppp2ppp/3p1n2/4p3/3PP3/5NP1/PPP2PBP/RNBQK2R b KQkq - 0 5.
- Here is a link to the FEN representation of the above position: rn-bqkb1r/ppp2ppp/3p1n2/4p3/3PP3/5NP1/PPP2PBP/RNBQK2R b KQkq - 0 5
- rnbqkb1r/ppp2ppp/3p1n2/4p3/3P4/5NP1/PPP1PPBP/RNBQK2R w KQkq - 0 5
- rnbqkb1r/ppp2ppp/3p1n2/4p3/3P4/5NP1/PPP1PPBP/RNBQK2R w KQkq - 0 5
- rnbqkb1r/ppp2ppp/3p1n2/4p3/3P4/5NP1/PPP1PPBP/RNBQK2R b KQkq - 0 4
- Here is the FEN:g2k4f4 b8k4b5 d7k6d6 e7k6e5 f1k1f2 g8k8f6 UCI: g2k4f4 b8k4b5 d7k6d6 e7k6e5 f1k1f2 g8k8f6 Note that the FEN is a different notation than the UCI.
- Here is the FEN: g2g3 d7d6 f1g2 e7e5 g1f3 g8f6 d2d4 b8 The following are the chess pieces: g - knight g2 - bishop g3 - knight d - queen d7 - queen d6 - queen f - bishop f1 - bishop e - knight e7 - knight e5 - knight h - knight h2 - knight h3 - knight h4 - knight h5 - knight h6 - knight
- FEN: g2g3 d7d6 f1g2 e7e5 g1f3 g8f6 d2d4 b8 This is a standard position in chess, but not in FEN.
- ChessGPT-Base:The first answer gives a code snippet rather than a direct answer. The second and third answers try to convert UCI to FEN, but the result is wrong. In terms of morbidity, too much information was provided, including an irrelevant code snippet.
- ChessGPT-Chat: The third answer is correct. The first two answers have subtle errors and the chess colors (w/b) are mislabeled.
- RedPajama: Completely wrong, it gives the game record instead of FEN.
Table 24: Question on UCI to FEN conversion.
PGN annotation
In this task, the models were tasked to generate commentary for a given game recorded in PGN format. Example responses can be found in table 25.
1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7 15. Generate a step-by-step analysis
- This is a typical position in the Scotch Game. White has a lead in development and Black has a pawn weakness on e6. White's next move is 14. Bxe7.
- I believe you're looking for a program that will analyze the position and output the best move for both sides. I have a couple of suggestions: ChessBase: This is a commercial program, but it's free for the first 30 days. Scid vs. PC: This is free and open-source, but you need to download the source code. I have used both of these programs in the past.
- This is a pretty easy one. 1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7 15. Rad1
- 1. e4 e5 (Both players have opened with pawn moves 1. e4 and 1... e5, controlling the center and opening lines for their bishops and queen) 2. Nf3 d6 (Black has played pawn move 2... d6, which is a passive response to White's 2. Nf3) 3. d4 (White has played pawn move 3. d4, attacking Black's pawn on e5 and opening lines for the queen and bishop) exd4 (Black has captured White's pawn on d4 with his pawn on e5, surrendering the center to White) 4.
- 1. e4 e5 (Both players have opened with pawn moves to the e-file: 1. e4 and 1.... e5) 2. Nf3 d6 (Black has played the Philidor Defense, which is a solid, flexible response to 1. e4. Black aims to develop pieces and prepare for central pawn breaks) 3. d4 exd4 (Black captures the pawn on d4) 4. Nxd4 Nf6 (Black develops the knight and prepares to castle kingside) 5. Nc3 Qe7 (Black plays a queen move, attacking
- 15...Qxb2 (Black's best move) 16. Rab1 Qe5 (Black's only move to avoid losing material) 17. Qxe5 Nxe5 18. Rxb7 (White has a significant advantage due to Black's vulnerable king and misplaced knight on e5)
1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nxe4 11. Nxe4 Nd7 12. Bg5 Qb4 13. Qg4 Qd4 14. Qxe6+ Be7 15. Generate a step-by-step analysis
1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nd7 11. Bg5 Qb4 12. Qg4 Qd4 13. Qxe6+ Be7 14. Nd5 Nxd5 15. exd5 Nd7 16. c3 c6 17. Bf4 Qd8 18.
1. e4 e5 2. Nf3 d6 3. d4 exd4 4. Nxd4 Nf6 5. Nc3 Qe7 6. Bd3 d5 7. O-O dxe4 8. Re1 Be6 9. Nxe6 fxe6 10. Bxe4 Nd7 11. Bg5 Qb4 12. Qg4 Qd4 13. Qxe6+ Be7 14. Generate a step-by-step analysis
Table 25: Question on PGN annotation
- ChessGPT-Base: The first answer is a short review of the Scotch Game, but not a step-by-step analysis. The second answer mentions other software for game analysis, which is not what the user asked for. The third answer just repeats the PGN given.
- ChessGPT-Chat: The first two answers started a step-by-step game analysis but were interrupted and not completed, but the analysis given is reasonable. The third answer provides a follow-up move for 15...Qxb2, but the user does not provide this step.
- RedPajama: The first and third answers just repeat the PGN given, no analysis is provided. The second answer tries to provide a follow-up move, which is also irrelevant to the question, since the user just requested an analysis, not a follow-up move.
Figure 6: Similarity matrix of different chess opening PGN and text using ChessCLIP.
I ChessCLIP visualization
We present a visualization demo to better illustrate ChessCLIP's capability. Here we choose the chess opening as a test scenario and choose ten different chess openings. Ten chess opening PGNs and their corresponding names are arranged in sequence, so the diagonal cells in the entire similarity matrix should have the highest similarity. We present such a similarity matrix generated by ChessCLIP in Figure 6. The results exactly correspond to our expectations, which successfully validate the effectiveness of our ChessCLIP model.
J Potential directions
In this section we will describe several potential directions based on our dataset.
1. Dataset Augmentation and Fine-tuning: Researchers can explore dataset augmentation techniques to further enhance the diversity and size of the provided dataset. By introducing variations in game conditions, player strategies, or opening positions, researchers can create augmented datasets that can be used to fine-tune existing language models or train new models specifically tailored for chess-related tasks. This can potentially lead to improved performance in state tracking, value judgement, and policy evaluation.
2. Transfer Learning to Chess Variants: The dataset and benchmark provided in this study can serve as a valuable resource for transfer learning experiments to chess variants. Researchers can leverage the knowledge learned from the base chess task and apply it to chess variants such as Fischer Random Chess or Chess960. By fine-tuning or adapting pre-trained language models on the provided dataset, researchers can explore the capabilities of these models in handling different chess variants, including state tracking, value judgement, and policy evaluation
3. Reinforcement Learning/Planning with Language Models: Combining reinforcement learning or planning with language models trained on the provided dataset opens up exciting possibilities for improving chess-playing agents. Researchers can develop reinforcement learning algorithms that
Cosine similarity between text and image features
utilize the language model's state tracking ability to build more sophisticated and strategic agents. By training agents to interact with the language model in a dialogue-like manner, researchers can explore the potential of language-based reinforcement learning for chess-related tasks, such as move generation and evaluation..
4. Explainable AI in Chess: Given the language model's ability to generate human-readable outputs, researchers can investigate the application of explainable AI techniques in chess. By interpreting the model's generated moves or predictions, researchers can gain insights into the reasoning behind the model's decisions. This can lead to the development of explainable AI systems that provide justifications or explanations for their chess moves, aiding both players and analysts in understanding and learning from the model's decision-making process.
5. Multi-modal Approaches: Researchers can explore multi-modal approaches that combine textual and visual information for chess-related tasks. By incorporating board visualizations, game position images, or move sequence visualizations along with textual inputs, researchers can develop models that leverage both textual and visual cues to improve state tracking, value judgement, and policy evaluation in chess. This can open up avenues for multi-modal analysis and understanding of chess games, allowing models to capture and reason over both textual and visual representations simultaneously.
6. Chess Education and Tutorial Systems: The dataset can be utilized to develop educational tools and tutorial systems for chess players of different skill levels. Researchers can leverage the language model's expertise in state tracking, value judgement, and policy evaluation to provide interactive and personalized learning experiences. By tailoring the tutorial content and feedback based on individual player performance, researchers can create intelligent systems that assist in skill development and strategic improvement in chess.
7. Adversarial Attacks in Chess: With the increasing use of language models in critical applications like chess analysis and decision-making, it becomes essential to investigate potential vulnerabilities and develop defenses against adversarial attacks. Researchers can explore techniques to generate adversarial examples specifically targeted at chess-related tasks. By identifying weaknesses in language models' state tracking or policy evaluation abilities, researchers can enhance the robustness and security of these models.
8. Chess Game Generation: Researchers can utilize the provided dataset to develop models capable of generating new chess game sequences. By leveraging the language model's understanding of chess moves and game structures, researchers can explore generative models that can produce novel and diverse chess game sequences. This can be beneficial for various applications, including chess game analysis, training data generation, and even game generation for chess variants.
Overall, our dataset and benchmark offer numerous potential directions, ranging from dataset expansion and transfer learning to exploring chess variants, education, analysis, and game generation. These directions have the potential to advance the field of chess-related language modeling and provide valuable tools and resources for chess players and enthusiasts.
K Limitations and Potential Societal Impact
The availability of a comprehensive and diverse chess dataset presents both limitations and potential societal impacts that researchers should consider.
Limitations. While the chess dataset provided in this study is valuable, it is important to acknowledge its limitations. One limitation is the potential bias introduced by relying on historical Lichess matches from different time periods. This may result in variations in player strategies, popular openings, and game trends over time, potentially impacting the generalizability of the dataset. Additionally, it is worth noting that the dataset predominantly focuses on standard chess, and may not encompass the full spectrum of chess variants. Researchers interested in exploring niche or less-popular variants may need to gather additional data from specific sources to ensure comprehensive coverage of different chess variants. These considerations are crucial to ensure the validity and applicability of research findings based on the provided dataset.
Potential Societal Impact. The availability of a comprehensive and diverse chess dataset can have a significant societal impact. First and foremost, it can contribute to the development of more advanced and intelligent chess-playing agents. These agents can be utilized in various applications, such as chess analysis, training tools for players of different skill levels, and even as opponents for chess enthusiasts. The dataset can also facilitate the advancement of chess education by providing valuable resources for tutorials, interactive learning platforms, and strategic guidance. Additionally, the dataset can inspire research in the field of artificial intelligence, contributing to the development of innovative techniques that can be applied beyond the domain of chess. Lastly, the dataset can encourage the exploration of explainable AI methods in chess, enabling players to understand and learn from the reasoning behind the model's moves, thereby promoting transparency and trust in AI systems.